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Questions related to Categorization
The pan-country study could have further divisions/categorization based on different criteria. Any recommendations would be greatly appreciated. Thank you!
What is the most appropriate classification for the 12 identified compounds from the essential oil of a medicinal plant, which include:
1. Camphene,
2.Para-Cymene,
3. 1-Limonene,
4.Gamma-Terpinen,
5.Trans-Decalone,
6. Cuminic Aldehyde,
7. Cyclopentanone,
8. Acetyl phenyl carbinol,
9. 1-amino-1-o,
10. 5-Methyl-2-Phenyl indolizine,
11. Silicic acid,
12. 5-nitrobenzofuran-2-carboxylic
Is the categorization in the attached image correct?
1. Grounded Physical-Ontological Categories behind Physics
Grounding can be of various levels and grades. I speak of grounding all sorts of concepts, procedure principles, procedure methods, and theories in any system of thought and science. It is unnecessary in this context to discuss the grounding of highly derivative concepts that occur much later in theories than those that appear while founding them with best-grounded foundations. I go directly to the case of what should be called the most Categorial concepts behind physics, on which physics is grounded.
These Categorial concepts cannot be merely from within physics but should be directly related to and facilitating physics in as many of its aspects as possible. The success of foundational Categories consists in that they serve to ground as many aspects as possible of the particular science or system. Concepts strictly and exclusively physical or generally scientific cannot be as useful as notions from beyond in order to serve as Categories. Evidently, this is because no scientific discipline or system can be grounded on itself and hence on its own concepts. This is clearly also part of the epistemological and ontological implications of the work of Godel.
Grounded ontological Categories are such that they are inevitably and exhaustively grounded in the To Be of Reality-in-total as the only exhaustive implications of To Be. All other Categories, as far as possible, must be derivative of the most primary Categories. The more the number of Categories within the Categorial system that do not derive from the primary Categories the worse for the self-evidence of the science or system within it.
Grounding is exhaustive in the sense that the Categories that ground all physics need nothing else to be a concept than the To Be of Reality-in-total. To Be is the source of the Categories. It happens to be that there are two such Categories that are inevitably and exhaustively grounded. I call them Extension and Change. Clarifications of their meaning, ontological significance, and epistemological and physical implications and follow.
As I said, preferably grounding must be on the surest notion of all, which is existence. I prefer to term it To Be. As far as thought, feeling, and sensation are concerned, To Be is a notion in al of them. But principally To Be must belong to the whole of Reality, and not to a few things. If anything and/or all processes of Reality are existent, then what exist are the parts of existent Reality. The first minimum guarantee hereof should be that existence should be non-vacuous. Non-vacuous signifies that each possesses or contains whatever is possible within its existence in the given measurementally spatio-temporal context (which, as shall soon be clear, belong ontologically to the Extension-Change-wise existence of things).
3. Definitions of Universals, Extension-Change, Causality, and Unit Process
Even the minimum realism in thought, feeling, and sensation has for its principal conditions (1) the ontological primacy of universal qualities / natures that belong to groups of entities (processes), where the groups are also called natural kinds in the analytic tradition, and then (2) the ultimate simplicity and indivisibility of the universal characteristics that pertain to all existents. Contrary to the infinite divisibility of existent matter-energy, universals as the characteristics of existent matter-energy conglomerations (of togethernesses of unit Processes) are ontologically ideal universals, and hence indivisible. These universals are ideal not because of our idealisation of the characteristics, but instead because they are the general characteristics of the natural kinds to which each existent belongs. Thus, it is important to keep in mind that ontological universals are not our idealizations.
The properties of things are built out of these simple ontological universals in the natural kinds. The vague reflections of simple ontological universals within our minds are conceptually connotative universals, which are conceptual ideals. And their linguistic reflections in minds and all kinds of symbolic instruments are denotative universals.
Connotative and denotative universals are epistemological universals, formed epistemically from the little contact that minds have with the phenomena (“showings-themselves”) from some layers of processual objects from out there. The properties of existent processual things (matter-energy particulars) are vaguely reflected in minds and languages through the connotative and denotative instrumentalization of concepts in order to reflect the things via phenomena in terms of the data created by minds out of them. Any theory that permits ontological primacy to epistemological universals is one of a range of theories yielding primacy to the perceiving mind over the perceived objects. This is anathema in any scientific or philosophical science, because things are not vacua.
Non-vacuous existence implies that existents are extended. This is one of the most important characteristics of existents. Extension implies having parts, compositionality. Any extended existent’s parts impart impact to some others. This is Change. Only extended existents can exert impacts on any other. As a result, the object that exerts impact gets in itself some impact within, which is nothing but the proof that an impact by one extended part implies movements and impact formation by its parts too, as a result of the overall impact formation in question which contains the inner parts’ impact formation within. The latter need not always have its effects merely within the parts but instead also outwards.
Extension and Change are the highest, deepest, and most general characteristics of all existents. Interestingly, existence in Extension-Change is itself the process that we have so far named causation. Hence, anything non-vacuously existent has Extension and Change not separately but together. This is the meaning of Universal Causality. Physics cannot dispense with this pre-scientific universal Law. No more shall quantum physicists or scientists from other disciplines tell us that quantum physics has some sort of non-causality within! Any causal unit of existents in which the causal part and the effect part may be termed a process. Processuality is yet another important characteristic of existents, but we formulate it as Process, which represents the matter-energy units that there can be.
By this have clearly been set up three physical-ontological Categories of physics: Extension, Change, Causality, and Process. Space and time are merely epistemic categories. They cannot characterize existent processes. Ontological universals, as the characteristics of existent matter-energy conglomerations, are of togethernesses of unit Processes. Ontological universals are therefore ontologically ideal universals belonging (pertaining) to some natural kinds. The Categories as ontological universals belong to Reality-in-total, and not merely some natural kinds.
I am currently working on enhancing the H4rmony dataset, which aims to fine-tune Large Language Models in AI to align them with ecolinguistic values. I'm seeking this classification to assist in balancing the dataset regarding the range of environmental issues addressed.
I understand social categorization theory is a key area in social psychology, which is not my field. I have been studying Tajfel's influential research conducted mostly in the 1970s. I have found that his experiments use 13-15 year old boys. Can anyone help me understand why he chose this sample? Thanks!
Let's say I have two dimensions:
Dimension 1: 2 categories
Dimension 2: 4 categories
If I were to code information based on these dimensions and I wanted to compare the agreement (e.g., dimension 1 = 90% vs. dimension 2 = 70%), considering the difference of categories in each one, is there a statistic that weights by the number of categories?
Would it be more appropriate to dichotomise the categories of dimension 2 to calculate agreement? I mean calculating the agreement for each category of dimension 2 separately, so that it is "category 1 = yes or category 1 = no", "category 2 = yes or category 2 = no", etc., instead of calculating it for all 4 categories together. This way you would calculate the agreement for each category in dimension 2 separately, but the results could be compared with the results for dimension 1.
I would also appreciate bibliography on the topic.
Thank you!
All the research community clearly notice that a classification has been going on for scientifc journals into quartiles; as Q1, Q2, Q3, and Q4. What do you think about this classification and criteria of categorization in terms of paper quality?
Some journals regard systematic reviews, meta-analyses and RCTs as original studies while KAP studies are not considered original by them. Is their scientific basis to their categorization ?
2. if I have 200 respondent for a questionnaire of 25 items included in 4 domains ,on scale score from 0 to 5, so the lowest response will be 0 and the highest will be 125.which scientific way should I follow to make categorizations and why for example into three level (low, moderate or high)or just (weak or strong) or (very low ,low ,moderate, high, very high).
take resilience questionnaire for example highest will be 125
so can we say any score from 0 to 42 he has low level of resilience, from 43 to 84he has medium level of resilience, from 84 to 125 he has low level of resilience
3. that absolutely will be useless if there is categorization in the original questionnaire or not?
My first option was to use a figure to be able to represent through the space occupied by each category the frequency of interviewees (or the categorizations) related to that content. From a peer review, I am considering changing this figure to a table with three columns indicating:
1) category name
2) frequency of coded interviewees
3) quotes from interviews
I notice some variability in the literature on what is the best way to make graphical representations of results in qualitative studies.
I would like to carry out a study (Social-Economical Categorization) on multi datasets (text data from ISPs, hospitals, Government records agencies ) using any suitable data mining technique. I read that WEKA can do the job. I am still a newbie when it comes to data mining analysis and WEKA. Kindly advise on how best I can do this.
Hi folks,
Imagine I have a dataset of bacterial abundance at specific degrees of temperature. Each line is a different bacterial species and each column is the abundance at a specific temperature.
Then in my dataset I would have for example some taxa increasing with temperature, some decreasing, some having plateau at certain values and so on, with several different functions of bacterial abundance shaped by temperature.
The question is: how can I classify my bacterial species according to their relationships with temperature? e.g. cluster 1 = all species linearly decreasing with T; cluster 2 = all species linearly increasing with T; cluster 3 = all species increasing according to a sigmoidal curve; cluster 4 = all species increasing according to a saturation curve and so on.
Any cool method you would suggest?
Thanks a lot
i have done lcms of plant extract and analysed the data to generate a list of metabolites that are now needed to be categorized in categories such as anti diabetic, anti cancerous anti microbial and various others. can you please suggest a workflow other than manual literature search as the data is quite heavy to be processed manually.
I am doing a study on how programmers with different levels of expertise choose class names. A description was provided to the participants together with a list of possible class names for the described class (An example is attached). I need to statistically show that novices select the same class name as experts or otherwise. Which statistics can I use for this?
In an anatomic study, we suspect that our length measurement of an entity (1) is confounded by the length measurement of another entity (2). Entity 2, unlike variables such as age, smoking pack-year, etc. , has yet to be "stratified" into categories in the literature.
Before we attempt to make arbitrary categorization of entity 2, is there a way to disentangle the influence of entity 2 in the measurement of entity 1? Can this be performed through multiple linear regression?
I am doing a binary logistic regression , the outcome is dichotomous the answer is yes or no, I have several dependent variables some of them are dichotomous and others are categorical.
When I run the univariate binary logistic regression on spss taking the outcome versus the covariate without making categorization of the covariate the result was significant.
However, when I run it another time but with categorization of the covariate the result was insignificant. I have changed the reference from the last to the first but also the result was insignificant.
Would you please help me with this problem why it occured and how to solve
HbA1c is an important indicator of long-term glycemic control with the ability to reflect the cumulative glycemic history of the preceding two to three months. Also, HbA1c is measured as the ratio between glycated and non-glycated hemoglobin.
If we run an HbA1c test on a fresh sample and an archived sample (assume that both of them is the same sample), will the reading deviates a lot and affect the categorization of diabetes?
Development of mathematical and kinetic models would start by matching a categorical example to a closely related reaction to be studied. If the selected example satisfies the characteristics of the proposed reaction having been chosen from the global option, modelling could be executed more precisely.
Colour is a domain in which routine reliance on verbal coding for
memory may be expected be cause the same features (hue, lightness, and saturation) are central for both naming and categorization. For some other domains there is a demonstrable distinction be tween the features that are necessary for the naming of objects and those that are central to the object’s
conceptual representation.
Wikipedia describes Physics, lit. 'knowledge of nature' , as the natural science that studies matter, its motion and behavior through space and time, and the related entities of energy and force
But isn’t this definition a redundancy? Any visible object is made of matter and its motion is a consequence of energy applied. We might as well say, study of stuff that happens. But then, what does study entail?
Fundamentally, ‘physics’ is a category word, and category words have inherent problems. How broad or inclusive is the category word, and is the ordinary use of the category word too restrictive?
Is biophysics a subcategory of biology? Is econophysics a subcategory of economics? If, for example, biophysics combines elements of physics and biology, does one predominate as categorization? If, as in biophysics and econophysics and astrophysics there are overlapping disciplines, does the category word ‘physics’ gives us insight about what physics studies or obscure what physics studies?
Is defining what physics does more a problem of semantics (ascribing meaning to a category word) than of science?
Might another way of looking at it be this? Physics generally involves the detection of patterns common to different patterns in phenomena, including those natural, emergent, and engineered; if possible detecting fundamental principles and laws that model them, and when possible using mathematical notation to describe those principles and laws; if possible devising and implementing experiments to test whether hypothesized or observed patterns provide evidence for or give clues to fundamental principles and laws.
Maybe physics more generally just involves problem solving and the collection of inferences about things that happen.
Your views?
I want to know how multi-label classification work in detail. At the same time, how does the confusion matrix for multi-label classification is constructed? How does the performance measures such as accuracy, precision, recall, and f-measure is calculated for multi-label classification?
Though Anderson's 1976 classification is the most conventional LULC classification system, and at the same time NLCD 2011 being a subset of the earlier one, there is confusion with barren and water class level-2 classification. The major confusion is with class 7. Barren ( 71 Dry Salt Flats. 72 Beaches. 73 Sandy Areas other than Beaches....) What does the level-2 73 class mean? Is it the whole coastal line other than beaches (for instance, the yellow highlighted areas in the image attached)? What if the sand holds moisture content? Can't we classify it under the water class? If the latter case is acceptable, then it will be like violating Anderson's classification scheme. I have found that NLCD 2011 classification is a brief categorization whereas Anderson's is not. Can anyone suggest how to proceed with this confusion/issue?
Thank you very much in advance.
I'm doing a research on environmental transformational leadership and employee green behaviour. Ages of the respondents (Employees of a company) varies from 20 - 57 years? any idea how can I categorise ?
Thank you
Here I need an equation or ideas to narrow the left view on ranking and categorization system and to widen the right view on ranking and categorization system.
Hello dears,
In regression modelling process, somtimes we deal to make a categorization of a continuos variable ( DVs or IDVs), What are really the potential problems inherent of such transformation, on:
- Estimation results
- Precesion and accuracy
- Hypothesis tests...
Thank you so much for any response and clarification
i am doing literature review and i found that different paper use different categorization for face recognition techniques.
some paper introduced by "holistic approach, feature base, hybrid approach" and some paper use "appearance base, feature base, soft computing" also i found some other classification "template base".
which one is more reliable and popular in this field and is there unique classification that can cover all methods.
I submit *.pdb file at the server and it displays the angles in output and their categorization in favoured or unfavoured region but it doesn't display the ramachandran plot...I have tried a lot of time but same output
I am wondering if the Random Tree or Random Forest algorithms can efficiently work, if the used data set only contains Numeric/ integer input variables? Or it will perform some sort of categorization before using the classification algorithms.?
I am specifically interested in WEKA's implementation of the algorithms.
We are studying perception of students towards e learning. Statements related to perception were rated on 5 point likert scale varying from strongly disagree to strongly agree. We have calculated mean for each respondent. Now we need to categorize our respondents for their perception. Say for example we can categorize it as low, moderate and high.Is their any research based categorization for it?
I need to get learning about insects particularly about “termites' Identification key and scientific categorization” for morphological identification. I am confronting troubles in finding precise ID key of termites and their definite ordered arrangement up to species level?
Hi everyone,
I'm wondering if there are any methods or tools to group into categories a phenomenon's factors or elements . For example, the affinity diagram allows to put into families a set of elements with common points. Are there other tools for categorization (without considering the importance of factors)?
Thank you
Hi,
Does anyone know of a tool that accepts as an input a list of different genes and categorize them according to subcellular localization (e.g. which of the genes code to proteins that are found on the cellular surface, which are transcription factors and are in the nucleus, etc.)?
Thanks,
Akiva
Does anyone have access to histopathology images for cancer categorization?
Following are the conversion pathways for sustainable bio-jet fuel production.(Serial 1-4 are ASTM certified)
1.Hydro- processed Esters and Fatty (HEFA)
2. Fischer-Tropsch (FT)
3.Direct sugars to hydro carbon (DSHC)
4.Alcohol to jet (ATJ)
5. Pyrolysis
6. Hydrothermal Liquefaction
In order to make a business case we must link conversion techniques with the feed-stock (2nd Gen.)
Literature focuses on any one type of feed-stock. For example in agriculture waste, wheat straw would be considered for Gassfication and FT, but it can also be considered for Alcohol to jet or Direct sugars to hydrocarbon.
Here is my categorization:
1. HEFA-Algae and non-edible oil seeds
2. For all other pathways MSW, Forestry residue, agriculture waste, wood waste.
I
I've been tackling the clustering algorithm called affinity propagation and feel pretty confident of its results. If you want to get an overall idea of a set of data, you use Affinity Propagation which will return a set of exemplars and the data points associated with each exemplar. The exemplars are the subset of data that best describes the similarity matrix of the entire data set. For example if you give it 1000 images and construct a similarity matrix based on hue, it will return the subset of images that best describe the hue of ALL the pictures.
The problem with AP is that it's clustering results can be shaky. the problem lies in the similarity matrix itself, which included the diagonal values in this matrix which AP calls the preference values. These values can make or break the clustering algorithm. Have the preference value too low, then you have too many clusters. Have it too high and you have too few clusters. The trick is finding the happy medium which the developers say is any number between the lowest and highest similarity value, often times the median value. You can adjust AP between these two values until you get something you think represents the result you are looking for.
I want to "adjust" these preference values. Currently the algorithm has all preference values the same. But what if i could preference certain values to be a different value? Doing this gives bias to two images being clustered together, or at the very least biases a certain image to be an exemplar.
What I need is an algorithm that would provide these values, or biases. My first inclination is to look towards neural networks. The reason for this is that ANN does a good job at image categorization. What if I used ANN to train on data in order to get values I can use to "bias" my similarity matrix and preference values.?
In this discussion I want to get some insights that others might have on this idea and to make sure that I have two ideas that are compatible. The idea for a project is to see if I can enhance AP using ANN to give me a better set of preference values. Even further it would be interesting to see if ANN can give me a better similarity matrix.
Thanks for your participation.
From the searches that Research Gate undertakes, I have books & reviews listed as articles, & reviews naming me as author rather than reviewer. I would like to correct these erroneous entries..
Does the expansion of how we define and apply intersectionality silence the voices of progressive black women?
Intersectionality is the interconnected nature of social categorizations such as race, class, and gender as they apply to black women.
For context, please read the article below.
Nash, J. C. (2008). Re-thinking intersectionality. Feminist review, 89(1), 1-15
Good Day!I am new to Machine Learning and I am currently working on a prediction model to predict categories using different parameters. The first step is to identify the categories from the data set I have. Based on literature, there are three main parameters that could be useful in describing the data set. Should I base my clustering the parameters I would be using to describe the clusters (3 main parameters) or should I include all the parameters and just use the 3 parameters to describe the resulting clusters?
Hi,
I am creating a framework that can be used to prioritize candidate projects. To do the prioritization, I will use a total score value and compare it for each candidate project. This total score value is actually a weighted average of multiple factors (e.g., cost, location, etc.). For each project, each one of these factors will be assigned a value of 1, 3, or 5 to indicate its performance (where the higher, the better). For example, if the location of project A is very good, a value of 5 is assigned to the location factor of project A, and so on.
In this framework, I also need to divide the candidate projects into two categories: important and not important. To do that, I will use a total score value for each project and compare it with a predefined threshold value. That is, if the total score is greater than this threshold value, it will be classified as important, and if the total score is less than this threshold value, it will be classified as not important.
After doing the analysis, I found that the total score value is between between 1.46 and 4.08. The maximum allowable value for the total score is 5 (and this happens if all the factors used in the analysis are set at their high values), and the minimum allowable value is 1 (and this happens if all the factors are set at their low values).
I plotted the histogram (attached to this question) of the total score values and found nothing. Also, I tested the normality of the results and found them not normal (see attachments).
So, my question is how pick the best or "optimal" threshold value that will give the best division for these candidate projects?
I know one way is to use percentile values (e.g., 50th percentile value), but I am looking for something better.
Thank you.
The literature regarding the shortcomings of categorization of moderating variables and also the use of 1 SD above and below the mean is quite understood and clear. My concern, however, is that is there an alternative to the above two options, giving that using the full range of the continuous moderating variable, to me, will produce an infinite number of range along which X relation to Y varies. Will it be possible to graph the effect without some form of categorization of the continuous moderating variable?
Although, soft and hard sciences disciplines classification is considered to be outdated, I could not find a new and/or clear classification of academic disciplines under these two broad fields.
The right to land is at stake in the process of land acquisition for the public interest. Sometimes the existing land acquisition process does not respect the land rights, so it is necessary to reconceptualize the categorization of the rights.
Dear Sirs and Madams,
Your contribution to a collection of images depicting systems of organization for color and species will be used in a study. I am gathering images that represent graphically the organization of colors and species. The images may be from folk taxonomy, Linnaean taxonomy, iBOL, cladistics, Munsell, Goethe, and other sources.
If you add an image, please indicate its source. Thank you!
The images will provide the materials for a follow-up research I conducted on nomenclature of color and species (World to Word: Nomenclature Systems of Color and Species https://mospace.umsystem.edu/xmlui/bitstream/handle/10355/60517/Dissertation_2017_Kelley_replacement.pdf?sequence=5&isAllowed=y)
Tanya Kelley
Taxonomists,
What do you think about alternative systems of identification of species? Is DNA barcoding going to replace Linnaean binomial nomenclature? What are the advantages of a numeric system? Nomenclature is the topic of my dissertation and subject of my further research so I am interested in your opinions.
Tanya Kelley
In your opinion what are the future challenges in the field of text classification (categorization)?
I have several maps (based on mostly on interpolation of geological borehole data) of an underground area that has been mined extensively. These maps represent ‘explanatory variables’ of factors which in combination may to lessor and greater extents have impacted on actual mining conditions experienced. They include, for example, depth, proximity to water source, rock type etc.
I also have a map of the same area which represents the ‘response variable’ - the actual mining conditions encountered. The response variable is a simple 4 class categorization from ‘good response’ to ‘very poor response.
Would the ‘surface response methodology’ be appropriate to determine a best-fit formula to predict ground response based on the given maps (historical data)? If not, are there alternative methods?
Thanks!
I want to classify the temperature to check the stresses in the human embryo. Is there a standard method for this categorization?
I'm particularly interested in research looking at how categories license inferences, and how, by including objects in a category, people reason about them in specific ways.
Hi,
I will be running an experiment that requires participants to distinguish between several novel objects. Ideally, each novel object will be a configuration of 3D geometric shapes (e.g., pyramids, pentagonal-prisms, spirals, discs, cuboids) but objects *cannot* be distinguished from one another based on one particular local feature: the only defining aspect of an object should be its overall configuration.
For example, if we have object A (a configuration of a cuboid, cylinder, and a pyramid) for each of its features there will be at least one other novel object that contains the identical feature (e.g., object B might have the identical cuboid, object C might have the identical pyramid, and so on…) - and thus the objects cannot be differentiated based on local features, and must be differentiated by overall configuration instead. So I’m looking for a stimuli set where features have been manipulated systematically such that objects can be distinguished only by their configuration of features (something corresponding to the linked table would be ideal):
Has such a stimuli set has been used in the past, and if so has it been made available? Any suggestions welcome.
Ryan
I am working on a new research about scoliosis and I have a question and I need your advice. I know there is a cut off for angle of trunk rotation (ATR) measured by scoliometer for radiographic consultation and it is accepted as 5 degree or 7 degree according to different studies. But I've searched the literature and couldn't find. Does any classification exist like mild ATR, moderate ATR and severe ATR? I need some categorizations about ATR in my study, what do you suggest me?
I'm new to Qualitative data and trying to run an agreement analysis for a set of data with four different categories. However, the answers were coded from short interviews, so a lot of questions have a combination of two or three categories.
Specifically, at first, I was thinking to do category 1 = 1, category 2= 2, category 3 = 3, category 4 =4, combination =5, and layout the responses in a column for each rater (for SPSS). However, the combinations could be 1 and 2, 2 and 3, or 1,3, and 4...etc.
Could somebody help me understand I should deal with this type of data?
Dear Colleagues,
Hopefully this is quite a simple question:
I'm going to be running some masked semantic congruence priming studies, and am looking for suitable stimuli. Put simply, semantic congruence studies typically show that a target word (e.g., HAWK) is semantically categorised (e.g., Is this an animal?) faster when preceded by a category-congruent/semantically-related prime word (e.g., eagle) compared to when preceded by a semantically unrelated word (e.g., knee).
The first thing I want to do is to replicate the classic finding using a larger set of stimuli. I will need at least 90 target words, each with a semantically-related prime-word. In line with previous studies (e.g., Quinn & Kinoshita, 2008), a lot of my stimuli will be drawn from McRae et al.'s set of feature norms (which is particularly useful for identifying members of the 'animal' category that have high semantic feature overlap; e.g., cat-dog; sheep-goat; etc.). But to reach 90 targets (each with a semantically similar prime), I will probably need to find a similar, but more dense database.
Ideally, I'm after an easy userface where I can simply input a target word (e.g., hand) that belongs to a category I'm using for the categorisation task (e.g., is this a body-part?) and it provides a list of the most semantically similar words from that category (e.g., if the category is 'body parts' it might output 'head, ankle, shin, foot, etc.). I'm aware there are a few solutions out there - whether it be measures semantic feature overlap or co-occurrence (e.g., wordnet, COALS, LSA, HAL) but I'd favour something with an interface that is easy to use, or even just a large datafile similar to McRae's 2005 set.
Thanks a lot!
Ryan
Quinn, W.M. and Kinoshita, S. (2008) Congruence effect in semantic categorization with masked primes with narrow and broad categories. Journal of Memory and Language, 58, 286–306.
McRae, K., Cree, G. S., Seidenberg, M. S., & McNorgan, C. (2005). Semantic feature production norms for a large set of living and nonliving things. Behavior Research Methods, Instruments, and Computers, 37, 547–559.
I want to measure the relevance of word patterns and group them by applying similarity-based clustering algorithm for text categorization.
Thanks!
Clear categorization of Hurst Range, when it's really close to 0.5
One of the assumptions for continuous variables in logistic regression is linearity. I have a large matched case-control study (~300,000 records) where one variable has a right-skewed distribution and (of course) doesn't meet the linearity assumption. I've read that transformation is not a good option, but it is still used in the medical literature. Other researchers say that categorization is a bad idea. And, some suggest that the fractional polynomial or GAM should be used.
Is transformation really a bad idea?
Thanks for your suggestions,
/Christian
This is regarding evaluating IR systems.
Say, I need to classify a set of test data (in fact, text documents), in which some of them could belong to more than one subject category (categorization is based on text similarities between test and training sets).
Can anyone give me an idea how to evaluate the output performance for this kind of problem? If the test entities belong to single categories, then I know it can be easily done by comparing the relevancy of the given output with the original subject category of the input test document. But I am not sure how to handle it when the test entities fall under multiple categories. Is there any standard method?
Your response would be highly appreciated!
I'm trying to detect clusters of Lyme disease that are not explained by an area's "greenness." SaTScan cannot use continuous data for covariates, so I've created NDVI quintiles. The categories show some correlation with LD rate but I'm unsure if doing this invalidates the analysis.
Hi there,
I am working on a Project where ANSYS and MATLAB are coupled together to get modal shapes and to seperately categorize them according to the type of the Vibration (i.e longitudinal, bending hori. , bending vertical etc.). Hence for the categorization I need to obtain the nodal directional deformations of two nodes being printed on the Output file. So please help me with the Input command for that.
Thanks in advance
More often the categorization of echogenicity of organs on ultrasound image is done subjectively and this leads to a high rate of inter observer variability and misdiagnosis in some instances.
A Mathematical model approach to come up with a software that can subjective categorize echogenicity of organs is a major objective of this study can any one outhere help us out on this?
Hi... hello everyone...
Can anybody here suggest me the most reliable 'term weighting scheme' in text classification (other than the TF - IDF)? I'm looking for weighting scheme that recommends; the higher the weight, the more relevant the term is; which in my case the more probably severe it will be. This is needed as an input signal; such as in spam detection, document categorization, etc. Later on, these weights will be applied in signal processing in DCA or Dendritic Cell Algorithm from Danger Theory.
Many thanks.
When investigating more complex phenomena, data analysis can reveal concepts that can be developed to different levels of significance. For example, when developing categories you can distinguish categories, subcategories, their properties.
However, I have not find any recommendation in literature.
Comments and references to literature sources are welcome.
I want to describe on a generall level how technological change impact on different technologies of an organization. I hope, there exist a kind of categorization schema for various kinds of technologies independently from any industry affiliation.
Like website, ERP, Mobile apps are three different types of Information technology, if we are going to check the usability of ERP. ERP is a software which is mandatory to use by users(employees). As a result, frequency of usage is higher which might lead to better usability. In case of a website , usage could be optional by users. Same about eCommerce or eBanking software. Do you think in both of the cases, same method of Usability should be followed? Is there any term or theory available to differentiate Information technology based on use?
I want perform text categorization using topic models. In order to implement the existing systems to learn how they work, is it ok to use Libraries for this purpose or do I need to code the whole framework, so that the results could closely match.
thank you
There are many nested (partially or fully) communities in a complex network. Taking a country for example, cities are communities within the country, neighborhoods are communities within the cities, and families are communities within the neighborhoods. Eventually, there are far more small communities than large ones, or the community sizes demonstrate power laws or heavy tailed distributions, which we have empirically verified.
Jiang B. and Ma D. (2015), Defining least community as a homogeneous group in complex networks, Physica A, 428, 154-160
Jiang B., Duan Y., Lu F., Yang T. and Zhao J. (2014), Topological structure of urban street networks from the perspective of degree correlations, Environment and Planning B: Planning and Design, 41(5), 813-828.
However, communities detected by our algorithm hardly match to those by previous community detection methods. My question is, should communities be nested?
As I see it proximity is perhaps equated to such concepts as "vicinity" or within the "set"- measurable or amendable to description in more or less a fuzzy manner. While "distance" is about units of measurement?
I am particularly interested in whether there is an effect of the training type on the advantage of trained exemplars over the prototype.
Independent variables - one is categorical, the other is dichotomous and dependent variable is also dichotomous.
Which model of regression should I use? Please kindly suggest one.
Hello everyone,
I'm currently sitting with a data set where I have measured several physiological correlates over time. In this case I have a lot of measurements/repetitions: example - 500 baseline measurements, 2000 measurements during an intervention and 500 post intervention measurements. I see that several of my physiological correlates change during the intervention, but more interestingly, it seems that the relationship between these changes as well.
As an example: I have two outcome measures A & B that are linearly correlated during the baseline, but as I apply my intervention, the correlation shifts, or even disappears. I am thus interested in assessing how the correlation changes over time, and possible include a lot of additional outcome measures in the analysis to get a complete overview. What is the best way to assess this statistically?
I am interested to do perform such an analysis both within single subjects, but also at a group level. Finally, if possible, I would include an additional independent categorical variable as I have tested several interventions. Hope I made myself understandable?
Greetings,
Lars
I need a fast approach for object localization in kind of images used in the industry, which are related to different parts of car and the goal is to categorize them in different classes. At the first step, I want to detect the part in image and remove the background for making the rest of the process easier and faster. It shouldn't necessarily extract just the object and there is no problem if some part of the background is extracted as well, as long as the line that separate the detected part doesn't have any texture. for example it can be a rectangle or circle.
As this process should be done when an object (part of car) is crossing a conveyor belt, it must be a really fast process.
Thank you so much.
I want to analyse a set of data of one dichotomous variable but I have four factor of which three are different doses of plant growth regulators but the other one is type of culture media.
I am thinking in analyse the culture media like categorical variable.
Any Suggestions?
Thanks
I am researching on the distinction between manner-in-verb languages (e.g., English and German) and path-in-verb languages (e.g., Spanish and Greek). This issue has been researched extensively, both theoretically -- esp. by Dan Slobin -- and empirically -- e.g. by Lera Boroditsky and Anna Papafragou, with diverging results.
In relation to other areas in the battlefield of linguistic relativity (e.g., colour or gender categorization), the topic of motion seems to be the hardest 'nut to crack,' with research shedding totally opposite results. Now, I can think of two reasons for that, namely
(1) it is still unclear what speakers really focus on in their general attention to the world, as biased by their language. That is, where most research seems to indicate that we'll focus on what's encoded in our own language, there are also indications of the opposite (i.e. Papafragou, Hulbert & Trueswell 2008: 'participants spontaneously studied those aspects of the scene that their language does not routinely encode in verbs').
and (2) so-called manner-in-verb languages actually tend to encode path information with great frequency and detail (albeit as a satellite to the verb, e.g. the leaf floated OUT OF THE CAVE AND RIGHT INTO THE HOLE ON THE DRIFTING LOG), such that the language-specific dichotomy is a little blurry.
I would highly appreciate any opinions on these issues. Also, would you please direct me towards any recent publications of relevance (other than Gumperz&Levinson 1999, Boroditsky and Papafragou)? Thank you so much.