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A Direct Algorithm For The K-Nearest-Neighbor Classifier Via Local Warping Of The Distance Metric, Tohkoon Neo
A Direct Algorithm For The K-Nearest-Neighbor Classifier Via Local Warping Of The Distance Metric, Tohkoon Neo
Theses and Dissertations
The k-nearest neighbor (k-NN) pattern classifier is a simple yet effective learner. However, it has a few drawbacks, one of which is the large model size. There are a number of algorithms that are able to condense the model size of the k-NN classifier at the expense of accuracy. Boosting is therefore desirable for increasing the accuracy of these condensed models. Unfortunately, there does not exist a boosting algorithm that works well with k-NN directly. We present a direct boosting algorithm for the k-NN classifier that creates an ensemble of models with locally modified distance weighting. An empirical study conducted …
Heuristic Weighted Voting, Kristine Perry Monteith
Heuristic Weighted Voting, Kristine Perry Monteith
Theses and Dissertations
Selecting an effective method for combining the votes of classifiers in an ensemble can have a significant impact on the overall classification accuracy an ensemble is able to achieve. With some methods, the ensemble cannot even achieve as high a classification accuracy as the most accurate individual classifying component. To address this issue, we present the strategy of Heuristic Weighted Voting, a technique that uses heuristics to determine the confidence that a classifier has in its predictions on an instance by instance basis. Using these heuristics to weight the votes in an ensemble results in an overall average increase in …
Limitations And Extensions Of The Wolf-Phc Algorithm, Philip R. Cook
Limitations And Extensions Of The Wolf-Phc Algorithm, Philip R. Cook
Theses and Dissertations
Policy Hill Climbing (PHC) is a reinforcement learning algorithm that extends Q-learning to learn probabilistic policies for multi-agent games. WoLF-PHC extends PHC with the "win or learn fast" principle. A proof that PHC will diverge in self-play when playing Shapley's game is given, and WoLF-PHC is shown empirically to diverge as well. Various WoLF-PHC based modifications were created, evaluated, and compared in an attempt to obtain convergence to the single shot Nash equilibrium when playing Shapley's game in self-play without using more information than WoLF-PHC uses. Partial Commitment WoLF-PHC (PCWoLF-PHC), which performs best on Shapley's game, is tested on other …
Improving Neural Network Classification Training, Michael Edwin Rimer
Improving Neural Network Classification Training, Michael Edwin Rimer
Theses and Dissertations
The following work presents a new set of general methods for improving neural network accuracy on classification tasks, grouped under the label of classification-based methods. The central theme of these approaches is to provide problem representations and error functions that more directly improve classification accuracy than conventional learning and error functions. The CB1 algorithm attempts to maximize classification accuracy by selectively backpropagating error only on misclassified training patterns. CB2 incorporates a sliding error threshold to the CB1 algorithm, interpolating between the behavior of CB1 and standard error backpropagation as training progresses in order to avoid prematurely saturated network weights. CB3 …
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Parallelization Of Ant Colony Optimization Via Area Of Expertise Learning, Adrian A. De Freitas
Theses and Dissertations
Ant colony optimization algorithms have long been touted as providing an effective and efficient means of generating high quality solutions to NP-hard optimization problems. Unfortunately, while the structure of the algorithm is easy to parallelize, the nature and amount of communication required for parallel execution has meant that parallel implementations developed suffer from decreased solution quality, slower runtime performance, or both. This thesis explores a new strategy for ant colony parallelization that involves Area of Expertise (AOE) learning. The AOE concept is based on the idea that individual agents tend to gain knowledge of different areas of the search space …
Obstacle Avoidance And Path Traversal Using Interactive Machine Learning, Jonathan M. Turner
Obstacle Avoidance And Path Traversal Using Interactive Machine Learning, Jonathan M. Turner
Theses and Dissertations
Recently there has been a growing interest in using robots in activities that are dangerous or cost prohibitive for humans to do. Such activities include military uses and space exploration. While robotic hardware is often capable of being used in these types of situations, the ability of human operators to control robots in an effective manner is often limited. This deficiency is often related to the control interface of the robot and the level of autonomy that control system affords the human operator. This thesis describes a robot control system, called the safe/unsafe system, which gives a human operator the …
Cognitive And Behavioral Model Ensembles For Autonomous Virtual Characters, Jeffrey S. Whiting
Cognitive And Behavioral Model Ensembles For Autonomous Virtual Characters, Jeffrey S. Whiting
Theses and Dissertations
Cognitive and behavioral models have become popular methods to create autonomous self-animating characters. Creating these models presents the following challenges: (1) Creating a cognitive or behavioral model is a time intensive and complex process that must be done by an expert programmer (2) The models are created to solve a specific problem in a given environment and because of their specific nature cannot be easily reused. Combining existing models together would allow an animator, without the need of a programmer, to create new characters in less time and would be able to leverage each model's strengths to increase the character's …