Peter E. Hart’s research while affiliated with New York State and other places

... Machine learning is intrinsically more powerful than traditional statistical approaches since it allows for conclusions or judgments that would not otherwise be achievable using classic statistical methods, although it still heavily relies on statistics and probability. Recently, machine learning has been used to support cancer prognosis and prediction [76]. New techniques for early cancer prediction are needed because conventional procedures are inaccurate and unsuitable for individualized care. ...

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Role of artificial intelligence in cancer detection using protein p53: A Review

... For both classification and regression, results were validated with repeated stratified 10-fold cross validation [30] (with 10 repetitions) and the average obtained performance is reported. ...

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Emotionally-Relevant Features for Classification and Regression of Music Lyrics

... Clustering algorithms have been studied for several decades [DH73], and they remain one of the main ingredients in unsupervised learning [DHS01]. Intuitively, a cluster is both a geometric concept (e.g., a lowdimensional region in a high-dimensional space) and a probabilistic concept (e.g., a region of the input space in which the sample data density is high). ...

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Clustering, coding, and the concept of similarity

... Use of nonmetric information extraction or AI methods allows the computer to analyse data perhaps better than people. The benefits of using AI for image analysis involve the use of expert systems that place all the information contained within an image in its proper context with ancillary data and then to extract valuable information (Duda, Hart & Stork, 2001). ...

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Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities

... The signals are then downsampled by a factor of 4. The xDAWN spatial filter [44] was used to reduce the 32 EEG channels to 3 xDAWN components that maximise the difference between target and non-target trials. A Linear Discriminant Analysis (LDA) classifier [45] uses epochs from stimulus-onset to 600 ms post-stimulus to determine the target row and column, i.e. the row and column that contain the letter the user is focusing on. Both the xDAWN spatial filter and the LDA classifier are trained for each participant at the beginning of the session by using the EEG data recorded from copyspelling runs. ...

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Accelerating P300-based neurofeedback training for attention enhancement using iterative learning control: a randomized controlled trial

... A physical version of his abstract world, Shakey's "real environment," was constructed for him within SRI's facilities. Resembling a poorly designed open office layout with linoleum floors, low walls, and an acoustical ceiling with fluorescent lighting laid out to minimize shadows, the space was designed for Fig. 9 A diagram of Shakey and his components (Nilsson 1984;Nilsson et al. 1969;Raphael et al. 1971) the benefit of Shakey's vision system, with paint colors and other high-contrast finishes (especially the dark baseboard) to help his fledgling image processing and edge-detection algorithms. The objects that Shakey was tasked with navigating around, moving, and organizing were rectangular or wedge-shaped and painted in alternating specular paint colors on each face, also to emphasize their edges. ...

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Robot excess: machine histories and a hermeneutics of movement

... One of the best-known examples for classification is spam / no-spam detection for emails (cf. [47]). Regression is the problem in which a continuous value for each item needs to be predicted. ...

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Applications of machine learning in the brewing process: a systematic review

... Unfortunately, this particular statement, which is similar to others we have encountered elsewhere, has no factual basis. [19] Dendral's team even suggested that the lack of feedback they got from users was an indication of successful use (rather than -as most producers of programs would realise -a lack of use of the program) by writing: ...

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The rise and fall of the legal expert system†† Previously published in Leith P., ‘The rise and fall of the legal expert system’, in European Journal of Law and Technology, Vol 1, Issue 1, 2010.View all notes

... Furthermore, in [54], the popular density-based algorithm DBSCAN [34] was observed to identify a significant portion of DLPs as noise, suggesting more work may be necessary to make density-based methods effective with time series. Hidden Markov Models [95] fitted using the Expectation Maximisation algorithm [33], alongside -means with DTW and Barycenter averaging, were also initially considered as candidate indivisible clustering approaches for this study. However, due to unpromising preliminary results and significant computational demands for both methods, they were ultimately excluded. ...

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Comparing Clustering Approaches for Smart Meter Time Series: Investigating the Influence of Dataset Properties on Performance

... The use of FIS in exploration of deposits is also not a new idea. This method was developed by different scientists [6,[50][51][52]. However, the data used in this work was selected according to relevance with respect to the porphyry copper exploration criteria. ...

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The geochemical exploration of orogenic gold deposit using