Machine Learning

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About the Machine Learning Group (Last updated 13 Jul 2001)

The Laboratory for Knowledge Discovery in Databases (KDD) is a research group in the Computing and Information Sciences (CIS) Department at Kansas State University. Its research emphasis is in the areas of applied artificial intelligence (AI) and knowledge-based software engineering (KBSE) for decision support systems.

More specifically, we are interested in machine learning, data mining and knowledge discovery from large spatial and temporal databases, human-computer intelligent interaction (HCII), and high-performance computation in learning and optimization. In our research, we look for ways to systematically decompose analytical learning problems based upon information theoretic and probabilistic criteria, so that the most appropriate machine learning methods may be applied to the resulting transformed problems.

One of the major challenges in this area is the design of unsupervised learning and bias (or hyperparameter) optimization methods to produce an effective decomposition of learning tasks. An interesting opportunity presented by this problem is that, by addressing the high-level control of inductive learning in a statistically sound fashion, we can improve our techniques for both model selection and model integration (as practiced in multimodal sensor fusion). We have developed and applied such approaches to multistrategy learning, which are potentially computation-intensive, to interesting analytical problems in the areas of decision support (uncertain reasoning) and control automation.

The goal of our work is to gain insight into the interaction between artifacts that adapt or learn - whether by Bayesian, neural, or genetic computation - and their users. Important examples of this interaction include data visualization in intelligent displays, software agents for distributed high-performance computation and information retrieval, and virtual environments for simulation and computer-assisted instruction.

Currently our projects are primarily focusing on the reimplementation of a subset of MLC++ into MLJ and the implementation of wrappers for performance enhancements in KDD. In doing these projects, it is our intent to better understand the workings of different induction alogrithms, and to build upon them for furture research.

Resources Online (Last updated 13 Jul 2001)

Machine learning resource page (maintained by D. Aha, Naval Research Lab)

This page is the most comprehensive machine learning resource guide on the web. It covers applications and experimental data sets as well as professional organizations, tutorials, and publications.

Projects (Last updated 29 Jan 2002)

Porting MLC++ [Kohavi] to MLJ

Makefile - Makefile for compiling MLJ sources.



ID3



C4.5



Simple Bayes

Feature Subset Selection Wrappers

FSS - Feature Subset Selection Wrapper



CHC [Eshelman, 1991] is a generational genetic search algorithm that uses half uniform crossover, cataclysmic mutation, and elitist selection as its search strategy.



TDCI [Donoho and Rendell] - Theory Domain Constructive Induction

Presentations (Last updated 13 Jul 2001)

Journal Papers

[under construction]

Conference Papers

[under construction]

Technical Reports

[under construction

White Papers

[under construction]

Publications (Last updated 11 Apr 2001)

Work in Progress (Last updated 18 April 2002)

Timetables / Milestones

May 10, 2002 : Tenetative Date of Alpha Release

To-Do Lists

Data Sets

Software Releases

Documentation

MLJ API

Group Members and Affiliates (Last updated 22 Jan 2002)

Faculty and Affiliates

Graduate Students

Undergraduate Students

Alumni



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