Digital Commons Network^™

Dynamically Optimized Context In Recommender Systems, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

Traditional approaches to recommender systems have not taken into account situational information when making recommendations, and this seriously limits the relevance of the results. This paper advocates context-awareness as a promising approach to enhance the performance of recommenders, and introduces a mechanism to realize this approach. We present a framework that separates the contextual concerns from the actual recommendation module, so that contexts can be readily shared across applications. More importantly, we devise a learning algorithm to dynamically identify the optimal set of contexts for a specific recommendation task and user. An extensive series of experiments has validated that our …

Improving And Extending Behavioral Animation Through Machine Learning, Jonathan J. Dinerstein

Theses and Dissertations

Behavioral animation has become popular for creating virtual characters that are autonomous agents and thus self-animating. This is useful for lessening the workload of human animators, populating virtual environments with interactive agents, etc. Unfortunately, current behavioral animation techniques suffer from three key problems: (1) deliberative behavioral models (i.e., cognitive models) are slow to execute; (2) interactive virtual characters cannot adapt online due to interaction with a human user; (3) programming of behavioral models is a difficult and time-intensive process. This dissertation presents a collection of papers that seek to overcome each of these problems. Specifically, these issues are alleviated …

Evaluating Online Trust Using Machine Learning Methods, Weihua Song

Doctoral Dissertations

Trust plays an important role in e-commerce, P2P networks, and information filtering. Current challenges in trust evaluations include: (1) fnding trustworthy recommenders, (2) aggregating heterogeneous trust recommendations of different trust standards based on correlated observations and different evaluation processes, and (3) managing efficiently large trust systems where users may be sparsely connected and have multiple local reputations. The purpose of this dissertation is to provide solutions to these three challenges by applying ordered depth-first search, neural network, and hidden Markov model techniques. It designs an opinion filtered recommendation trust model to derive personal trust from heterogeneous recommendations; develops a reputation …

Learning Discrete Hidden Markov Models From State Distribution Vectors, Luis G. Moscovich

LSU Doctoral Dissertations

Hidden Markov Models (HMMs) are probabilistic models that have been widely applied to a number of fields since their inception in the late 1960’s. Computational Biology, Image Processing, and Signal Processing, are but a few of the application areas of HMMs. In this dissertation, we develop several new efficient learning algorithms for learning HMM parameters. First, we propose a new polynomial-time algorithm for supervised learning of the parameters of a first order HMM from a state probability distribution (SD) oracle. The SD oracle provides the learner with the state distribution vector corresponding to a query string. We prove the correctness …

An Assessment Of Case-Based Reasoning For Spam Filtering, Sarah Jane Delany, Padraig Cunningham, Lorcan Coyle

Articles

Because of the changing nature of spam, a spam filtering system that uses machine learning will need to be dynamic. This suggests that a case-based (memory-based) approach may work well. Case-Based Reasoning (CBR) is a lazy approach to machine learning where induction is delayed to run time. This means that the case base can be updated continuously and new training data is immediately available to the induction process. In this paper we present a detailed description of such a system called ECUE and evaluate design decisions concerning the case representation. We compare its performance with an alternative system that uses …

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

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

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.

Knowledge Discovery In Environmental Data Bases Using Gesconda, Karina Gibert, Xavier Flores, Ignasi Rodríguez-Roda, Miquel Sànchez-Marrè

International Congress on Environmental Modelling and Software

In this work, last results of the research project “Development of an Intelligent Data Analysis System for Knowledge Management in Environmental Data Bases (DB)” are presented. The project is focussed on the design and development of a prototype for Knowledge Discovery (KD) and intelligent data analysis, and specially oriented to environmental DB. It is remarkable the high quantity of information and knowledge patterns that are implicit in large DB coming from environmental domains. In this project, several environmental DB such as meteorological phenomena, wastewater treatment plants (WWTP), or environmental emergencies were used for testing. KD is a prior and mandatory …

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 …

Symbolic Regression Of Thermo-Physical Model Using Genetic Programming, Ying Zhang

USF Tampa Graduate Theses and Dissertations

The symbolic regression problem is to find a function, in symbolic form, that fits a given data set. Symbolic regression provides a means for function identification. This research describes an adaptive hybrid system for symbolic function identification of thermo-physical model that combines the genetic programming and a modified Marquardt nonlinear regression algorithm.

Genetic Programming (GP) system can extract knowledge from the data in the form of symbolic expressions, i.e. a tree structure, that are used to model and derive equation of state, mixing rules and phase behavior from the experimental data (properties estimation).

During the automatic evolution process of GP, …

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

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 …

Reconstructability Analysis: Theory And Applications [Editorial Introduction], Martin Zwick, Guangfu Shu, Yi Lin

Complex Systems Faculty Publications and Presentations

Reconstructability analysis (RA) dates back to the pioneering work of Ashby in the mid-1960s. In the 1970s and 1980s, RA was the subject of very active research in the systems community. It receded for a time as a focus of activity, but the special issue of the International Journal of General Systems in 1996 on the General Systems Problem Solver and the special IJGS issue in 2000 on Reconstructability Analysis in China marked the renewal of interest in this area. The current volume is part of this resurgence of activity. It collects together papers from the group at Portland State …

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 …

Graph-Theoretic Techniques For Web Content Mining, Adam Schenker

USF Tampa Graduate Theses and Dissertations

In this dissertation we introduce several novel techniques for performing data mining on web documents which utilize graph representations of document content. Graphs are more robust than typical vector representations as they can model structural information that is usually lost when converting the original web document content to a vector representation. For example, we can capture information such as the location, order and proximity of term occurrence, which is discarded under the standard document vector representation models. Many machine learning methods rely on distance computations, centroid calculations, and other numerical techniques. Thus many of these methods have not been applied …

On Machine Learning Methods For Chinese Document Classification, Ji He, Ah-Hwee Tan, Chew-Lim Tan

Research Collection School Of Computing and Information Systems

This paper reports our comparative evaluation of three machine learning methods, namely k Nearest Neighbor (kNN), Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM) for Chinese document categorization. Based on two Chinese corpora, a series of controlled experiments evaluated their learning capabilities and efficiency in mining text classification knowledge. Benchmark experiments showed that their predictive performance were roughly comparable, especially on clean and well organized data sets. While kNN and ARAM yield better performances than SVM on small and clean data sets, SVM and ARAM significantly outperformed kNN on noisy data. Comparing efficiency, kNN was notably more costly …

Machine Learning Approaches For Determining Effective Seeds For K -Means Algorithm, Kaveephong Lertwachara

Doctoral Dissertations

In this study, I investigate and conduct an experiment on two-stage clustering procedures, hybrid models in simulated environments where conditions such as collinearity problems and cluster structures are controlled, and in real-life problems where conditions are not controlled. The first hybrid model (NK) is an integration between a neural network (NN) and the k-means algorithm (KM) where NN screens seeds and passes them to KM. The second hybrid (GK) uses a genetic algorithm (GA) instead of the neural network. Both NN and GA used in this study are in their simplest-possible forms.

In the simulated data sets, I investigate two …

Efficient Decomposition Of Large Fuzzy Functions And Relations, Paul Burkey, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This paper presents a new approach to decomposition of fuzzy functions. A tutorial background on fuzzy logic representations is first given to emphasize next the simplicity and generality of this new approach. Ashenhurst-like decomposition of fuzzy functions was discussed in [3] but it was not suitable for programming and was not programmed. In our approach, fuzzy functions are converted to multiple-valued functions and decomposed using an mv decomposer. Then the decomposed multiple-valued functions are converted back to fuzzy functions. This approach allows for Curtis-like decompositions with arbitrary number of intermediate fuzzy variables, that have been not presented for fuzzy functions …

Machine Learning Techniques For Efficient Query Processing In Kowledge Base Systems, Kevin Paul Grant

LSU Doctoral Dissertations

In this dissertation we propose a new technique for efficient query processing in knowledge base systems. Query processing in knowledge base systems poses strong computational challenges because of the presence of combinatorial explosion. This arises because at any point during query processing there may be too many subqueries available for further exploration. Overcoming this difficulty requires effective mechanisms for choosing from among these subqueries good subqueries for further processing. Inspired by existing works on stochastic logic programs, compositional modeling and probabilistic heuristic estimates we create a new, nondeterministic method to accomplish the task of subquery selection for query processing. Specifically, …

A New Approach To Robot’S Imitation Of Behaviors By Decomposition Of Multiple-Valued Relations, Uland Wong, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

Relation decomposition has been used for FPGA mapping, layout optimization, and data mining. Decision trees are very popular in data mining and robotics. We present relation decomposition as a new general-purpose machine learning method which generalizes the methods of inducing decision trees, decision diagrams and other structures. Relation decomposition can be used in robotics also in place of classical learning methods such as Reinforcement Learning or Artificial Neural Networks. This paper presents an approach to imitation learning based on decomposition. A Head/Hand robot learns simple behaviors using features extracted from computer vision, speech recognition and sensors.

Probabilistic Finite Automata And Randomness In Nature: A New Approach In The Modelling And Prediction Of Climatic Parameters, L. Mora-López, R. Morales-Bueno, M. Sidrach-De-Cardona, F. Triguero

International Congress on Environmental Modelling and Software

A model to characterize and predict continuous time series from machine learning techniques isproposed. This model includes the following three steps: dynamic discretization of continuous values,construction of probabilistic finite automata and prediction of new series with randomness. The first problemin most models from machine learning is that they are developed for discrete values; however, mostphenomena in nature are continuous. To convert these continuous values into discrete values a dynamicdiscretization method has been used. With the obtained discrete series, we have built probabilistic finiteautomata which include all the representative information which the series contain. The use of probabilisticfinite automata allows us …

Plant Disease Models. Critical Issues In Development And Use, A. Susi, A. Perini, E. Olivetti

International Congress on Environmental Modelling and Software

Models which simulate the evolution of a plant disease during the season give important informationto assess the seriousness of the situation. This activity precedes the choice of an appropriate action to be implementedfor reducing the economical damage. Having effective models is a critical issue in modern agriculture,especially in Integrated Protection and Organic Farming, which rest on a set of plant disease managementpractices with low environmental impact.Considerable effort has gone in the study of models for the simulation of plant diseases evolution. Phenologymodels, population and epidemiological models have been developed for several, diffused diseases. Problemsare still open, especially when the aim …

Environmental Data Mining And Modelling Based On Machine Learning Algorithms And Geostatistics, M. Kanevski, R. Parkin, A. Pozdnukhov, V. Timonin, M. Maignan, B. Yatsalo, S. Canu

International Congress on Environmental Modelling and Software

The paper presents some contemporary approaches to the spatial environmental data analysis, processing and presentation. The main topics are concentrated on the decision–oriented problems of environmental and pollution spatial data mining and modelling: valorisation and representativity of data with the help of exploratory data analysis, topological, statistical and fractal measures of monitoring networks, spatial predictions and classifications, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The set of tools used consists of machine learning algorithms (MLA) – Multilayer Perceptron, General Regression Neural Networks, Probabilistic Neural Networks, Radial Basis Function Networks, Support Vector Machines and Support Vector …

Machine-Learned Contexts For Linguistic Operations In German Sentence Realization, Eric K. Ringger, Simon Corston-Oliver, Michael Gamon, Robert Moore

Faculty Publications

We show that it is possible to learn the contexts for linguistic operations which map a semantic representation to a surface syntactic tree in sentence realization with high accuracy. We cast the problem of learning the contexts for the linguistic operations as classification tasks, and apply straightforward machine learning techniques, such as decision tree learning. The training data consist of linguistic features extracted from syntactic and semantic representations produced by a linguistic analysis system. The target features are extracted from links to surface syntax trees. Our evidence consists of four examples from the German sentence realization system code-named Amalgam: case …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …

Machine Learning And Small Robot Navigation, Christopher Ireland

Senior Scholar Papers

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing …

An Analysis Of The Effectiveness Of A Constructive Induction-Based Virus Detection Prototype, Kevin T. Damp

Theses and Dissertations

Computer viruses remain a tangible threat to systems both within the Department of Defense and throughout the greater international data communications infrastructure on which the DoD increasingly depends. This threat is exacerbated continually, as new viruses are introduced at an alarming rate by the growing collection of connected machines and their operators. Unfortunately, current antivirus solutions are ill-equipped to address these issues in the long term. This thesis documents an investigation into the use of constructive induction, a form of machine learning, as a supplemental antivirus technique theoretically capable of detecting previously unknown viruses through generalized decision-making techniques. A group …

Using Knowledge-Based Neural Networks In The Prediction Of Permeability, William K. Zollinger

Graduate Student Theses, Dissertations, & Professional Papers

No abstract provided.

Multiple Stochastic Learning Automata For Vehicle Path Control In An Automated Highway System, Cem Unsal, Pushkin Kachroo, John S. Bay

Electrical & Computer Engineering Faculty Research

This paper suggests an intelligent controller for an automated vehicle planning its own trajectory based on sensor and communication data. The intelligent controller is designed using the learning stochastic automata theory. Using the data received from on-board sensors, two automata (one for lateral actions, one for longitudinal actions) can learn the best possible action to avoid collisions. The system has the advantage of being able to work in unmodeled stochastic environments, unlike adaptive control methods or expert systems. Simulations for simultaneous lateral and longitudinal control of a vehicle provide encouraging results