22 Result(s)
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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 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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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 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 ...
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Chapter and Conference Paper
Learning from inconsistent and noisy data: The AQ18 approach
In concept learning or data mining tasks, the learner is typically faced with a choice of many possible hypotheses characterizing the data. If one can assume that the training data are noise-free, then the gen...
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Chapter and Conference Paper
Detecting targets in SAR images: A machine learning approach
This paper describes a novel application of the MIST methodology to target detection in SAR images. Specifically, a polarimetric whitening filter and a constant false alarm rate detector are used to preprocess...
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Chapter and Conference Paper
Learning for decision making: The FRD approach and a comparative study
This paper concerns the issue of what is the best form for learning, representing and using knowledge for decision making. The proposed answer is that such knowledge should be learned and represented in a decl...
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Chapter and Conference Paper
The AQ17-DCI system for data-driven constructive induction and its application to the analysis of world economics
Constructive induction divides the problem of learning an inductive hypothesis into two intertwined searches: one-for the “best” representation space, and two-for the “best” hypothesis in that space. In datadrive...
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Chapter and Conference Paper
Learning problem-oriented decision structures from decision rules: The AQDT-2 system
A decision structure is an acyclic graph that specifies an order of tests to be applied to an object (or a situation) to arrive at a decision about that object. and serves as a simple and powerful tool for org...
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Chapter and Conference Paper
Should decision trees be learned from examples or from decision rules?
A standard method for determining decision trees is to learn them from examples. A disadvantage of this approach is that once a decision tree is learned, it is difficult to modify it to suit different decision...
