Machine Learning and Inference Laboratory, College of Public Health
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The Machine Learning and Inference (MLI) Laboratory conducts fundamental and experimental research on the development of intelligent systems capable of advanced forms of learning, inference, and knowledge generation, and applies them to real-world problems.
Major research areas include:
- theory and computational models of learning and inference
- data mining and knowledge discovery
- machine learning and natural induction
- inductive databases and knowledge scouts
- behavior modeling and computer intrusion detection
- non-Darwinian evolutionary computation
- multistrategy learning and knowledge mining
- intelligent systems for education
- models of human plausible reasoning
- machine vision with learning capabilities
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Recent Submissions
Item Progress Report on the Learnable Evolution Model(2007-11-18T03:39:21Z) Michalski, Ryszard S.; Wojtusiak, Janusz; Kaufman, KennethThis report reviews recent research on Learnable Evolution Model (LEM), and presents selected results from its application to the optimization of complex functions and engineering designs. Among the most significant new contributions is a multi operator methodology for generating individuals (candidate solutions) and the employment of a more advanced learning program, AQ21, as the learning module. The new features have been implemented in the LEM3 program. To evaluate LEM3’s performance, it was experimentally compared to other evolutionary computation programs, such as, EA--a conventional, Darwinian-type evolutionary computation program, CA--a cultural evolution algorithm, and EDA--an estimation of distribution algorithm on selected function optimization problems. To determine the scalability of LEM3 and compared programs, the number of variables in the optimized functions was varied from 2 up to 1000. In every experiment, LEM3 outperformed the other programs in terms of the evolution length, sometimes more than an order of magnitude. Another recent research result is the development of early versions of two LEM-based systems, ISHED and ISCOD, for the optimization of heat exchangers evaporators and condensers, respectively. This work was done in collaboration with scientists from the National Institute of Science and Technology. In experimental testing, the systems produced designs that matched or were superior to human designs, particularly, in the cases of nonuniform air flows. This collaboration continues, and may ultimately produce systems that NIST will use to develop better designs of heat exchangers and have them implemented by the industry.Item Semantic and Syntactic Attribute Types in AQ Learning(2007-11-18T03:33:50Z) Michalski, Ryszard S.; Wojtusiak, JanuszAQ learning strives to perform natural induction that aims at deriving general descriptions from specific data and formulating them in human-oriented forms. Such descriptions are in the forms closely corresponding to simple natural language statements, or are transformed to such statements in order to make computer generated knowledge easy to interpret and understand. An important feature of natural induction is that it employs a wide range of types of attributes to guide the process of generalization. Attribute types constitute problem domain knowledge, and are provided by the user, or are inferred by the learning program from the data. This paper makes a distinction between semantic and syntactic attribute types in AQ learning, explains their relationships and provides their classifications. Semantic types depend solely on the structure of attribute domains and help to create plausible generalizations, while syntactic types depend also on physical properties of attribute domains, and are used to efficiently implement semantic types.Item A Measure of Description Quality for Data Mining and its Implementation in the AQ18 Learning System(1999-06) Michalski, Ryszard S.; Kaufman, Kenneth A.Item AQ-PM: A Method for Partial Memory Learning(1999-06) Maloof, Marcus A.; Michalski, Ryszard S.Item Discovering Multidimensional Patterns in Large Datasets Using Knowledge Scouts(1999-06) Kaufman, Kenneth A.; Michalski, Ryszard S.Item Learning from Inconsistent and Noisy Data: The AQ18 Approach(1999-06) Kaufman, Kenneth A.; Michalski, Ryszard S.Item An Experimental Application of the Learnable Evolution Model and Genetic Algorithms to Parameter Estimation in Digital Signal Filters Design(1999-05) Coletti, Mark; Lash, Thomas D.; Mandsager, Craig; Moustafa, Rida E.; Michalski, Ryszard S.Item Initial Experiments with the LEM1 Learnable Evolution Model: An Application to Function Optimization and Evolvable Hardware(1999-05) Michalski, Ryszard S.; Zhang, QiItem Learning in an Inconsistent World: Rule Selection in AQ18(1999-05) Kaufman, Kenneth A.; Michalski, Ryszard S.Item Discovery Planning: Multistrategy Learning in Data Mining(1998-06) Kaufman, Kenneth A.; Michalski, Ryszard S.Item Learnable Evolution: Combining Symbolic and Evolutionary Learning(1998-06) Michalski, Ryszard S.Item Data-Driven Constructive Induction(1998-03) Bloedorn, Eric; Michalski, Ryszard S.Item An Overview of Research Activities in the Machine Learning and Inference Laboratory: 1997-1998(1998-01) Zhang, Qi; Michalski, Ryszard S.Item Seeking Knowledge in the Deluge of Facts(1997) Michalski, Ryszard S.Item El Nino Teleconnections Research: Initial Results Using a Machine Learning and Discovery Approach(1997) Li, Zuotao; Kafatos, Menas; Michalski, Ryszard S.Item Computer Vision through Learning(1997) Maloof, Marcus A.; Rosenfeld, Azriel; Duric, Zoran; Aloimonos, Yiannis; Zhang, Qi; Michalski, Ryszard S.Item An Overview of Research Activities in the Machine Learning and Inference Laboratory: 1996-1997(1997) Michalski, Ryszard S.Item Multistrategy Data Exploration Using the INLEN System: Recent Advances(1997-06) Michalski, Ryszard S.; Kaufman, Kenneth A.Item EMERALD 2: An Integrated System of Machine Learning and Discovery Programs for Education and Research, Programmer's Guide for the Sun Workstation (Updated Edition)(1997) Kaufman, Kenneth A.; Michalski, Ryszard S.Item EMERALD 2: An Integrated System of Machine Learning and Discovery Programs for Education and Research, User's Guide (Updated Edition)(1997) Kaufman, Kenneth A.; Michalski, Ryszard S.