Richard O. Duda’s research while affiliated with New York State and other places

... k-Nearest Neighbors (k-NN) Regressor: k-NN Regressor predicts continuous values by averaging the output of the k-nearest neighbors in the feature space, offering simplicity and flexibility while requiring proper distance metric selection (Duda et al. 2000). The hyperparameters include 'n_neighbors', the number of neighbors considered (1-20), 'weights', defining neighbor contribution (uniform or distance), 'algorithm', selecting the method for finding closest points (auto, ball_tree, kd_tree, or brute), and 'leaf_size', impacting tree-based search speed (typically 10-50). ...

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Machine learning (ML) algorithms for prediction of low temperature co-fired ceramic (LTCC) diaphragm performance

... 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

... Risk classi…cation, cancer detection, object detection, outlier detection, image classi…cation are some applied areas in classi…cation methods. Over the last decade, many statistical methods have been applied including linear regression, logistic regression (LR), neural networks (NNet), Naive Bayes (NB), k-nearest neighbor (kNN), Support Vector Machine (SVM), boosting methods and other approaches [1,2]. The methods are usually based on optimization problems comprised loss functions. ...

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A CORRECTION ON TANGENTBOOST ALGORITHM

... with m the sample size and A = K + σ 2 y I. Once D and θ are known we can restrict the joint distribution in equation (6) to only describe functions which accommodate the observations in D, inducing the PPD. This is done by conditioning on this joint distribution using D, θ and the input locations of unknown function values X * , ...

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Edge Tracing Using Gaussian Process Regression

... This search is then repeated with the found pixel, forming a chain of pixels until the contour is complete. An example of this is the algorithm described by Duda et al. in Reference [13]. The algorithm starts by tracing each pixel of the image, for example, column-by-column from the bottom row to the top row, until a black pixel is encountered. ...

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Particle Swarm Contour Search Algorithm

... A deeper understanding of technology also positively influences the perceived ease of use [37] and affects attitudes toward it [38,39]. AI is often associated with negative attitudes, e.g., related to employee displacement [40,41]. However, Bruckermann et al. [42] showed that acquiring specific knowledge in the context of citizen science projects promotes positive attitudes toward this research. ...

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Effects of AI Understanding-Training on AI Literacy, Usage, Self-Determined Interactions, and Anthropomorphization with Voice Assistants

... These techniques are the simplest form of artificial intelligence and mimic the reasoning of a human expert in solving a knowledge-intensive problem. In other words, AI-RBSs encode human expert knowledge about a specific topic into an automated system; they are often used to comprise an expert system [43,44]. An AI-RBS reproduces deductive reasoning mechanisms by employing logic rules made of conjunctions of conditions to verify a set of actions to execute [45]. ...

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A General Rule-Based Framework for Generating Alternatives for Forest Ecosystem Management Decision Support Systems

... In Ref. 12. all the character recognition information is processed bottom~up. ...

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UNDERSTANDING HANDWRITTEN TEXT IN A STRUCTURED ENVIRONMENT: DETERMINING ZIP CODES FROM ADDRESSES

... This condition ensures that for some ✓ 2 R d , the dataset can be perfectly classified. The quantity µ is a margin (Novikoff 1962;Duda, Hart, and G.Stork 2001), which characterizes the distance between the two classes. This assumption is practical in modern machine learning problems (Soudry et al. 2018;Ji and Telgarsky 2018). ...

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From Logistic Regression to the Perceptron Algorithm: Exploring Gradient Descent with Large Step Sizes

... In the 1980s, the focus shifted to expert systems, AI programs that used knowledge bases to mimic human expertise in specific domains. 12 Additionally, neural networks gained popularity as a model for machine learning. 13 ...

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Artificial intelligence in oral and maxillofacial surgery: A road ahead