55 Result(s)
within
Ryszard S. Michalski
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Article
Reasoning with unknown, not-applicable and irrelevant meta-values in concept learning and pattern discovery
This paper describes methods for reasoning with unknown, irrelevant, and not-applicable meta-values when learning concept descriptions from examples or discovering patterns in data. These types of meta-values rep...
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Chapter and Conference Paper
Learning and Evolution: An Introduction to Non-darwinian Evolutionary Computation
The field of evolutionary computation has drawn inspiration from Darwinian evolution in which species adapt to the environment through random variations and selection of the fittest. This type of evolutionary ...
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Chapter and Conference Paper
Generalizing Data in Natural Language
This paper concerns the development of a new direction in machine learning, called natural induction, which requires from computer-generated knowledge not only to have high predictive accuracy, but also to be in ...
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Chapter
Recent Advances in Conceptual Clustering: CLUSTER3
Conceptual clustering is a form of unsupervised learning that seeks clusters in data that represent simple and understandable concepts, rather than grou**s of entities with high intra-cluster and low inter-c...
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Chapter and Conference Paper
An Integrated Multi-task Inductive Database VINLEN: Initial Implementation and Early Results
A brief review of the current research on the development of the VINLEN multitask inductive database and decision support system is presented. The aim of this research is to integrate a wide range of knowledge...
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Chapter and Conference Paper
The Use of Compound Attributes inAQ Learning
Compound attributes are named groups of attributes that have been introduced in Attributional Calculus (AC) to facilitate learning descriptions of objects whose components are characterized by different subset...
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Chapter and Conference Paper
Learning Symbolic User Models for Intrusion Detection: A Method and Initial Results
This paper briefly describes the LUS-MT method for automatically learning user signatures (models of computer users) from datastreams capturing users’ interactions with computers. The signatures are in the for...
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Chapter and Conference Paper
A Rules-to-Trees Conversion in the Inductive Database System VINLEN
Decision trees and rules are completing methods of knowledge representation. Both have advantages in some applications. Algorithms that convert trees to rules are common. In the paper an algorithm that convert...
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Chapter and Conference Paper
Knowledge Visualization Using Optimized General Logic Diagrams
Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space. Knowledge can be in...
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Article
Introduction
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Chapter and Conference Paper
The Development of the Inductive Database System VINLEN: A Review of Current Research
Current research on the VINLEN inductive database system is briefly reviewed and illustrated by selected results. The goal of research on VINLEN is to develop a methodology for deeply integrating a wide range of
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Chapter and Conference Paper
Modeling User Behavior by Integrating AQ Learning with a Database: Initial Results
The paper describes recent results from develo** and testing LUS methodology for user modeling. LUS employs AQ learning for automatically creating user models from datasets representing activities of compute...
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Chapter and Conference Paper
Incremental Learning with Partial Instance Memory
Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. We extend our previous work by combining our method for selecting...
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Chapter and Conference Paper
A Knowledge Scout for Discovering Medical Patterns: Methodology and System SCAMP
Knowledge scouts are software agents that autonomously synthesize knowledge of interest to a given user (target knowledge) by applying inductive database operators to a local or distributed dataset. This paper...
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Chapter and Conference Paper
Discovering Multi-head Attributional Rules in Large Databases
A method for discovering multi-head attributional rules in large databases is presented and illustrated by results from an implemented program. Attributional rules (a.k.a. attributional dependencies) can be vi...
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Chapter
Learning Patterns in Noisy Data: The AQ Approach
In concept learning and data mining, a typical objective is to determine concept descriptions or patterns that will classify future data points as correctly as possible. If one can assume that the data contain...
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Article
Selecting Examples for Partial Memory Learning
This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these example...
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Article
An Adjustable Description Quality Measure for Pattern Discovery Using the AQ Methodology
In concept learning and data mining tasks, the learner is typically faced with a choice of many possible hypotheses or patterns characterizing the input data. If one can assume that training data contain no no...
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Chapter and Conference Paper
Speeding Up Evolution through Learning: LEM
This paper reports briefly on the development of a new approach to evolutionary computation, called the Learnable Evolution Model or LEM. In contrast to conventional Darwinian-type evolutionary algorithms that...
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Chapter and Conference Paper
Inductive Databases and Knowledge Scouts
The development of very large databases and the world wide web has created extraordinary opportunities for monitoring, analyzing and predicting global economical, ecological, demographic, political, and other ...
