Digital Commons Network^™

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 …

A Machine Learning Approach To Anomaly Detection, Philip K. Chan, Matthew V. Mahoney, Muhammad H. Arshad

Electrical Engineering and Computer Science Faculty Publications

Much of the intrusion detection research focuses on signature (misuse) detection, where models are built to recognize known attacks. However, signature detection, by its nature, cannot detect novel attacks. Anomaly detection focuses on modeling the normal behavior and identifying significant deviations, which could be novel attacks. In this paper we explore two machine learning methods that can construct anomaly detection models from past behavior. The first method is a rule learning algorithm that characterizes normal behavior in the absence of labeled attack data. The second method uses a clustering algorithm to identify outliers.

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 …

Scavenger: A Junk Mail Classification Program, Rohan V. Malkhare

USF Tampa Graduate Theses and Dissertations

The problem of junk mail, also called spam, has reached epic proportions and various efforts are underway to fight spam. Junk mail classification using machine learning techniques is a key method to fight spam. We have devised a machine learning algorithm where features are created from individual sentences in the subject and body of a message by forming all possible word-pairings from a sentence. Weights are assigned to the features based on the strength of their predictive capabilities for spam/legitimate determination. The predictive capabilities are estimated by the frequency of occurrence of the feature in spam/legitimate collections as well as …

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.

Development Of An Intelligent Data Analysis System For Knowledge Management In Environmental Data Bases, Miquel Sànchez-Marrè, Karina Gibert, Ignasi Rodríguez-Roda, Eva Bueno, Lidia Mozo, Aleix Clavell, Mario Martína, Philippe Rougé

International Congress on Environmental Modelling and Software

In this work, the goals of the ongoing research project “Development of an Intelligent Data Analysis System for Knowledge Management in Environmental Data Bases” are presented. This project started in 2000, and the Spanish Government will finance it until 2003. Its main goal is to design and develop a prototype of a tool for intelligent data analysis and implicit knowledge management of databases, with special focus on environmental databases. It is remarkable the high quantity of information and knowledge patterns implicit in large databases coming from the monitoring of any system or dynamical environmental process. For instance, historical data collected …

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 …

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 …

Implicit Algorithms For Multi-Valued Input Support Manipulation, Alan Mishchenko, Craig Files, Marek Perkowski, Bernd Steinbach, Christina Dorotska

Electrical and Computer Engineering Faculty Publications and Presentations

We present an implicit approach to solve problems arising in decomposition of incompletely specified multi-valued functions and relations. We introduce a new representation based on binaryencoded multi-valued decision diagrams (BEMDDs). This representation shares desirable properties of MDDs, in particular, compactness, and is applicable to weakly-specified relations with a large number of output values. This makes our decomposition approach particularly useful for data mining and machine learning. Using BEMDDs to represent multi-valued relations we have developed two complementary input support minimization algorithms. The first algorithm is efficient when the resulting support contains almost all initial variables; the second is efficient when …

Decomposition Of Relations: A New Approach To Constructive Induction In Machine Learning And Data Mining -- An Overview, Marek Perkowski, Stanislaw Grygiel

Electrical and Computer Engineering Faculty Publications and Presentations

This is a review paper that presents work done at Portland State University and associated groups in years 1989 - 2001 in the area of functional decomposition of multivalued functions and relations, as well as some applications of these methods.

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.

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson

Electrical and Computer Engineering Faculty Publications and Presentations

"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only.

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

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson

Electrical and Computer Engineering Faculty Publications and Presentations

"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only. Various approaches to supervised inductive learning …

Simulation Study Of Learning Automata Games In Automated Highway Systems, Cem Unsal, Pushkin Kachroo, John S. Bay

Electrical & Computer Engineering Faculty Research

One of the most important issues in Automated Highway System (AHS) deployment is intelligent vehicle control. While the technology to safely maneuver vehicles exists, the problem of making intelligent decisions to improve a single vehicle’s travel time and safety while optimizing the overall traffic flow is still a stumbling block. We propose an artificial intelligence technique called stochastic learning automata to design an intelligent vehicle path controller. Using the information obtained by on-board sensors and local communication modules, two automata are capable of learning the best possible (lateral and longitudinal) actions to avoid collisions. This learning method is capable of …

Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad

Theses and Dissertations

This research presents an autonomous and computationally tractable method for scientific process analysis, combining an iterative algorithmic search and a recognition technique to discover multivariate linear and non-linear relations within experimental data series. These resultant data-driven relations provide researchers with a potentially real-time insight into experimental process phenomena and behavior. This method enables the efficient search of a potentially infinite space of relations within large data series to identify relations that accurately represent process phenomena. Proposed is a time series transformation that encodes and compresses real-valued data into a well-defined, discrete-space of 13 primitive elements where comparative evaluation between variables …

Utilizing Data And Knowledge Mining For Probabilistic Knowledge Bases, Daniel J. Stein Iii

Theses and Dissertations

Problems can arise whenever inferencing is attempted on a knowledge base that is incomplete. Our work shows that data mining techniques can be applied to fill in incomplete areas in Bayesian Knowledge Bases (BKBs), as well as in other knowledge-based systems utilizing probabilistic representations. The problem of inconsistency in BKBs has been addressed in previous work, where reinforcement learning techniques from neural networks were applied. However, the issue of automatically solving incompleteness in BKBs has yet to be addressed. Presently, incompleteness in BKBs is repaired through the application of traditional knowledge acquisition techniques. We show how association rules can be …

Intelligent Control Of Vehicles: Preliminary Results On The Application Of Learning Automata Techniques To Automated Highway System, Cem Unsal, John S. Bay, Pushkin Kachroo

Electrical & Computer Engineering Faculty Research

We suggest an intelligent controller for an automated vehicle to plan its own trajectory based on sensor and communication data received. Our intelligent controller is based on an artificial intelligence technique called learning stochastic automata. The automaton can learn the best possible action to avoid collisions using the data received from on-board sensors. The system has the advantage of being able to work in unmodeled stochastic environments. Simulations for the lateral control of a vehicle using this AI method provides encouraging results.

Convergence Properties Of Perceptrons, Ratnasri Krishna Adharapurapu

Theses Digitization Project

No abstract provided.

A Fortran Based Learning System Using Multilayer Back-Propagation Neural Network Techniques, Gregory L. Reinhart

Theses and Dissertations

An interactive computer system which allows the researcher to build an optimal neural network structure quickly, is developed and validated. This system assumes a single hidden layer perceptron structure and uses the back- propagation training technique. The software enables the researcher to quickly define a neural network structure, train the neural network, interrupt training at any point to analyze the status of the current network, re-start training at the interrupted point if desired, and analyze the final network using two- dimensional graphs, three-dimensional graphs, confusion matrices and saliency metrics. A technique for training, testing, and validating various network structures and …

Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera

Theses and Dissertations

Computer-generated autonomous agents in simulation often behave predictably and unrealistically. These characteristics make them easy to spot and exploit by human participants in the simulation, when we would prefer the behavior of the agent to be indistinguishable from human behavior. An improvement in behavior might be possible by enlarging the library of responses, giving the agent a richer assortment of tactics to employ during a combat scenario. Machine learning offers an exciting alternative to constructing additional responses by hand by instead allowing the system to improve its own performance with experience. This thesis presents NOSTRUM, a discovery-based learning DBL system …

Discovery Learning In Autonomous Agents Using Genetic Algorithms, Edward O. Gordon

Theses and Dissertations

As the new Distributed Interactive Simulation (DIS) draft standard evolves into a useful document and distributed simulations begin to emerge that implement parts of the standard, there is renewed interest in available methods to effectively control autonomous aircraft agents in such a simulated environment. This investigation examines the use of a genetics-based classifier system for agent control. These are robust learning systems that use the adaptive search mechanisms of genetic algorithms to guide the learning system in forming new concepts (decision rules) about its environment. By allowing the rule base to evolve, it adapts agent behavior to environmental changes. Addressed …

Procase: A Prototype Of Intelligent Case-Based Process Planning System With Simulation Environment, Hao Yang, Wen F. Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

An intelligent case-based process planning system with interactive graphic simulation environment, PROCASE, is developed lo demonstrate an integrated methodology of case-based process planning system. In PROCASE, both the mechanical part features and the machining operations are represented with a frame-based scheme. PROCASE contains a retriever, a modifier, a simulator and a repairer. It distinguishes itself from traditional rule-based process planning systems by representing the process planning knowledge through previous process planning cases instead of production rules. It therefore can overcome some problems in the traditional rule-based expert systems. PROCASE currently resides in IRIS Indigo workstation. With a user-friendly graphic environment, …

Learning Algorithms For Artificial Neural Networks: A Closer Look At Back Propagation, Simplex And Monte Carlo, Sergio Alberto Cedeño

Graduate Theses and Dissertations

No abstract provided.

Real Time Maze Traversal, Robert Spina

Theses

This thesis investigates the application of artificial intelligence to real time control in combination with image analysis techniques. The system consists of a "mouse" and a "cheese" in a two dimensional maze which are viewed by a video camera connected to a frame grabber board in a personal computer (PC). The maze is designed to be alterable during the mouse's navigation towards the "cheese" or target. The pc runs the frame grabber software which analyzes the maze to find the cheese, mouse and movement options during traversal. Parameters about the mouse's position, the maze constraints in the vicinity of the …

Intelligent Knowledge Acquisition System, Bong-Soo Youn

Theses

IKAS (Intelligent Knowledge Acquisition System) is an expert system building shell with a machine-assisted concept checker for easy integrated rule-entry. Unlike most expert system shells currently available, IKAS not only performs rule syntax checking, but also maintains semantic integration of the knowledge base by using a Concept Base. The Concept Base is incrementally grown during the cycle of expert system application development and safeguards the whole domain from conceptually conflicting rules by finding possible inconsistencies or duplications of concepts and giving recommendations for possible solutions. Also automatic generalized concept forming using the Inductive Extension Generalization method gives IKAS a capability …

Learning System, Bhavesh T. Adani

Theses

Many rule-based learning systems have been implemented which generate and update rules/facts from illustrative examples provided by the user. But very few of them have succeeded in achieving the efficiency and accuracy of an ideal system.

The approach made here is to continuously update the hierarchical structure of rules/facts of a knowledge-base so that efficient and accurate solutions to any query can be achieved easily and immediately from the many possible solutions to the query. As the rule-based systems deal with a very large knowledge-base, it is sometimes necessary to apply a learning technique that finds optimum solutions efficiently.

The …

Artificial Intelligence: Myths And Realities, Hugo D'Alarcao

Bridgewater Review

Artificial intelligence the name conjures images of mechanical monsters, the Golem, Dr. Frankenstein’s creation and the rebellious computer Hal. We have always been fascinated by the possibility of creating a machine in our image, but this fascination is often accompanied by apprehension. We fear losing control of our creation and suspect that it might turn against us. It is this duality, this conflict between the desire to create and the fear of the consequences of the creation that has been so successfully exploited by writers. It is also, in part, this fascination that has recently brought the field of Artificial …