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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
A Bayesian Technique For Task Localization In Multiple Goal Markov Decision Processes, James Carroll, Kevin Seppi
A Bayesian Technique For Task Localization In Multiple Goal Markov Decision Processes, James Carroll, Kevin Seppi
Faculty Publications
In a reinforcement learning task library system for Multiple Goal Markov Decision Process (MGMDP), localization in the task space allows the agent to determine whether a given task is already in its library in order to exploit previously learned experience. Task localization in MGMDPs can be accomplished through a Bayesian approach, however a trivial approach fails when the rewards are not distributed normally. This can be overcome through our Bayesian Task Localization Technique (BTLT).
Vision-Based Human Directed Robot Guidance, Richard B. Arthur
Vision-Based Human Directed Robot Guidance, Richard B. Arthur
Theses and Dissertations
This paper describes methods to track a user-defined point in the vision of a robot as it drives forward. This tracking allows a robot to keep itself directed at that point while driving so that it can get to that user-defined point. I develop and present two new multi-scale algorithms for tracking arbitrary points between two frames of video, as well as through a video sequence. The multi-scale algorithms do not use the traditional pyramid image, but instead use a data structure called an integral image (also known as a summed area table). The first algorithm uses edge-detection to track …
Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke
Using Permutations Instead Of Student’S T Distribution For P-Values In Paired-Difference Algorithm Comparisons, Tony R. Martinez, Joshua Menke
Faculty Publications
The paired-difference t-test is commonly used in the machine learning community to determine whether one learning algorithm is better than another on a given learning task. This paper suggests the use of the permutation test instead hecause it calculates the exact p-value instead of an estimate. The permutation test is also distribution free and the time complexity is trivial for the commonly used 10-fold cross-validation paired-difference test. Results of experiments on real-world problems suggest it is not uncommon to see the t-test estimate deviate up to 30-50% from the exact p-value.
Solving Large Mdps Quickly With Partitioned Value Iteration, David Wingate
Solving Large Mdps Quickly With Partitioned Value Iteration, David Wingate
Theses and Dissertations
Value iteration is not typically considered a viable algorithm for solving large-scale MDPs because it converges too slowly. However, its performance can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We present several methods designed to help structure value dependency, and present a systematic study of companion prioritization techniques which focus computation in useful regions of the state space. In order to scale to solve ever larger problems, we evaluate all enhancements and methods in the context of parallelizability. Using the enhancements, we discover that in many instances the limiting …
Machine Learning Techniques For Characterizing Ieee 802.11b Encrypted Data Streams, Michael J. Henson
Machine Learning Techniques For Characterizing Ieee 802.11b Encrypted Data Streams, Michael J. Henson
Theses and Dissertations
As wireless networks become an increasingly common part of the infrastructure in industrialized nations, the vulnerabilities of this technology need to be evaluated. Even though there have been major advancements in encryption technology, security protocols and packet header obfuscation techniques, other distinguishing characteristics do exist in wireless network traffic. These characteristics include packet size, signal strength, channel utilization and others. Using these characteristics, windows of size 11, 31, and 51 packets are collected and machine learning (ML) techniques are trained to classify applications accessing the 802.11b wireless channel. The four applications used for this study included E-Mail, FTP, HTTP, and …
Using Symbolic Knowledge In The Umls To Disambiguate Words In Small Datasets With A Naive Bayes Classifier, Gondy Leroy, Thomas C. Rindflesch
Using Symbolic Knowledge In The Umls To Disambiguate Words In Small Datasets With A Naive Bayes Classifier, Gondy Leroy, Thomas C. Rindflesch
CGU Faculty Publications and Research
Current approaches to word sense disambiguation use and combine various machine-learning techniques. Most refer to characteristics of the ambiguous word and surrounding words and are based on hundreds of examples. Unfortunately, developing large training sets is time-consuming. We investigate the use of symbolic knowledge to augment machine-learning techniques for small datasets. UMLS semantic types assigned to concepts found in the sentence and relationships between these semantic types form the knowledge base. A naïve Bayes classifier was trained for 15 words with 100 examples for each. The most frequent sense of a word served as the baseline. The effect of increasingly …
Visual Expectations: Using Machine Learning To Identify Patterns In Psychological Data, Skyler Place
Visual Expectations: Using Machine Learning To Identify Patterns In Psychological Data, Skyler Place
Honors Theses
The goal of this project was to utilize the tools of machine learning to evaluate the data obtained through experiments in psychology. Advanced pattern finding algorithms are an effective approach to analyzing large sets of data, from any domain of science. Consequently, we have a psychological question and hypothesis, and a separate machine learning technique to assess these claims. The realm of psychology that I focused on is visual cognition, and how an individual's knowledge affects how they see the world. This alteration of visual data is a part of perception -when the brain enhances the data coming in from …