Handling numerical features is quite an open problem for the symbolic approach to machine learning. Indeed, many systems have a limited applicability because of their impossibility to deal with numerical data. In this...
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ISBN:
(纸本)078034863X
Handling numerical features is quite an open problem for the symbolic approach to machine learning. Indeed, many systems have a limited applicability because of their impossibility to deal with numerical data. In this paper, we propose an approach for learning definitions of concepts from their examples, in the presence of numerical but also uncertain data. This approach fits in a First Order logic framework and its main characteristics are: (1) the use of fuzzy sets to represent numerical data and model uncertain features, and (2) an inductive learning process based on Rough Set Theory which is capable of handling uncertainty within the learning data. Compared to classical symbolic approaches to inductive learning, it differs in two main points: firstly, it becomes possible to represent both sharp and flexible concepts, and secondly the definitions of concepts that are learned are not deterministic but fuzzy. This approach has been implemented through the EAGLE system and evaluated on a real-world problem of organic chemistry. The results obtained show its good potentialities.
We present a new tool called INDWEB, based on inductive logic programming, that can learn some concepts that characterized interesting pages for a user or a group of users with respect to a set of criteria on these pa...
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ISBN:
(纸本)3540643834
We present a new tool called INDWEB, based on inductive logic programming, that can learn some concepts that characterized interesting pages for a user or a group of users with respect to a set of criteria on these pages but also on these users or group of users.
One remarkable progress of recent research in machine learning is inductive logic programming (ILP). In most ILP system, clause specialization is one of the most important tasks. Usually, the clause specialization is ...
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One remarkable progress of recent research in machine learning is inductive logic programming (ILP). In most ILP system, clause specialization is one of the most important tasks. Usually, the clause specialization is performed by adding a literal at a time using hill-climbing heuristics. However, the single-literal addition can be caught by local pits when more than one literal needs to be added at a time increase the accuracy. Several techniques have been proposed for this problem but are restricted to relational domains. In this paper, we propose a technique called structure subtraction to construct a set of candidates for adding literals, single-literal or multiple-literals. This technique can be employed in any ILP system using top-down specilization and is not restricted to relational domains. A theory revision system is described to illustrate the use of structural subtraction .
The clausal discovery engine CLAUDIEN is presented. CLAUDIEN is an inductive logic programming engine that fits in the descriptive data mining paradigm. CLAUDIEN addresses characteristic induction from interpretations...
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The clausal discovery engine CLAUDIEN is presented. CLAUDIEN is an inductive logic programming engine that fits in the descriptive data mining paradigm. CLAUDIEN addresses characteristic induction from interpretations, a task which is related to existing formalisations of induction in logic. In characteristic induction from interpretations, the regularities are represented by clausal theories, and the data using Herbrand interpretations. Because CLAUDIEN uses clausal logic to represent hypotheses, the regularities induced typically involve multiple relations or predicates. CLAUDIEN also employs a novel declarative bias mechanism to define the set of clauses that may appear in a hypothesis.
This paper discusses the role that background knowledge can play in building flexible multistrategy learning systems. We contend that a variety of learning strategies can be embodied in the background knowledge provid...
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This paper discusses the role that background knowledge can play in building flexible multistrategy learning systems. We contend that a variety of learning strategies can be embodied in the background knowledge provided to a general purpose learning algorithm. To be effective, the general purpose algorithm must have a mechanism for learning new concept descriptions that can refer to knowledge provided by the user or learned during some other task. The method of knowledge representation is a central problem in designing such a system since it should be possible to specify background knowledge in such a way that the learner can apply its knowledge to new information.
Three different formalizations of concept-learning in logic (as well as some variants) are analyzed and related. It is shown that learning from interpretations reduces to learning from entailment, which in rum reduces...
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Three different formalizations of concept-learning in logic (as well as some variants) are analyzed and related. It is shown that learning from interpretations reduces to learning from entailment, which in rum reduces to learning from satisfiability. The implications of this result for inductive logic programming and computational learning theory are then discussed, and guidelines for choosing a problem-setting are formulated. (C) 1997 Elsevier Science B.V.
When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a ver...
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When learning from very large databases, the reduction of complexity is extremely important. Two extremes of making knowledge discovery in databases (KDD) feasible have been put forward. One extreme is to choose a very simple hypothesis language, thereby being capable of very fast learning on real-world databases. The opposite extreme is to select a small data set, thereby being able to learn very expressive (first-order logic) hypotheses. A multistrategy approach allows one to include most of these advantages and exclude most of the disadvantages. Simpler learning algorithms detect hierarchies which are used to structure the hypothesis space for a more complex learning algorithm. The better structured the hypothesis space is, the better learning can prune away uninteresting or losing hypotheses and the faster it becomes. We have combined inductive logic programming (ILP) directly with a relational database management system. The ILP algorithm is controlled in a model-driven way by the user and in a data-driven way by structures that are induced by three simple learning algorithms.
Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theor...
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Pre-pruning and Post-pruning are two standard techniques for handling noise in decision tree learning. Pre-pruning deals with noise during learning, while post-pruning addresses this problem after an overfitting theory has been learned. We first review several adaptations of pre- and post-pruning techniques for separate-and-conquer rule learning algorithms and discuss some fundamental problems. The primary goal of this paper is to show how to solve these problems with two new algorithms that combine and integrate pre- and post-pruning.
We devise a method to generate descriptive classification rules of shape contours by using inductive learning. The classification rules are represented in the form of logic programs. We first transform input objects f...
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We devise a method to generate descriptive classification rules of shape contours by using inductive learning. The classification rules are represented in the form of logic programs. We first transform input objects from pixel representation into predicate representation. The transformation consists of preprocessing, feature extraction and symbolic transformation. We then use FOIL which is an indictive logicprogramming system to produce classification rules. Experiments on two sets of data were performed to justify our proposed method. Copyright (C) 1997 pattern Recognition Society.
In this paper, we design an algorithm to construct simply recursive programs from a finite set of good examples. (C) 1997 Published by Elsevier Science B.V.
In this paper, we design an algorithm to construct simply recursive programs from a finite set of good examples. (C) 1997 Published by Elsevier Science B.V.
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