Although top-down induction of decision trees is a very popular induction method, up till now it has mainly been used for propositional learning;relational decision tree learners are scarce. This dissertation discusse...
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Although top-down induction of decision trees is a very popular induction method, up till now it has mainly been used for propositional learning;relational decision tree learners are scarce. This dissertation discusses the application domain of decision tree learning and extends it towards the first order logic context of inductive logic programming.
This paper describes LPMEME, a new learning algorithm for inductive logic programming that uses statistical techniques to find first-order patterns. LPMEME takes as input examples in the form of logical facts and outp...
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Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. inductive learning methods are typically used to acq...
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Machine Learning from examples may be used, within Artificial Intelligence, as a way to acquire general knowledge or associate to a concrete problem solving system. inductive learning methods are typically used to acquire general knowledge from examples. Lazy methods are those in which the experience is accessed, selected and used in a problem-centered way. In this paper we report important approaches to inductive learning methods such as propositional and relational learners, with an emphasis in inductive logic programming based methods, as well as to lazy methods such as instance-based and case-based reasoning. (C) 1998 Elsevier Science B.V.
Prolog program synthesis can be made more efficient by using schemata which capture similarities in previously-seen programs. Such schemata narrow the search involved in the synthesis of a new program. We define a gen...
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Prolog program synthesis can be made more efficient by using schemata which capture similarities in previously-seen programs. Such schemata narrow the search involved in the synthesis of a new program. We define a generalization operator for forming schemata from programs and a downward refinement operator for constructing programs from schemata. These operators define schema-hierarchy graphs which can be used to aid in the synthesis of new programs. Algorithms are presented for efficiently obtaining least generalizations of schemata, for adding new schemata to a schema-hierarchy graph, and for using schemata to construct new programs. (C) 1998 Elsevier Science B.V.
Learning from "structured examples" is necessary in a number of settings, including inductive logic programming. Here we analyze a simple learning problem in which examples have non-trivial structure: specif...
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Learning from "structured examples" is necessary in a number of settings, including inductive logic programming. Here we analyze a simple learning problem in which examples have non-trivial structure: specifically, a learning problem in which concepts are strings over a fixed alphabet, examples are deterministic finite automata (DFAs), and a string represents the set of all DFAs that accept it. We show that solving this "dual" DFA learning problem is hard, under cryptographic assumptions. This result implies the hardness of several other more natural learning problems, including learning the description logic CLASSIC from subconcepts, and learning arity-two "determinate" function-free Prolog clauses from ground clauses. The result also implies the hardness of two formal problems related to the area of "programming by demonstration": learning straightline programs over a fixed operator set from input-output pairs, and learning straightline programs from input-output pairs and "partial traces".
In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a si...
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In this papers we examine the issue of learning multiple predicates from given training examples. A proposed MPL-CORE algorithm efficiently induces Horn clauses from examples and background knowledge by employing a single predicate learning module CORE. A fast failure mechanism is also proposed which contributes learning efficiency and learnability to the algorithm. MPL-CORE employs background knowledge that can be represented in intensional (Horn clauses) or extensional (ground atoms) forms during its learning process. With the fast failure mechanism, MPL-CORE outperforms previous multiple predicate learning systems in both the computational complexity and learnability.
Cooperative Information Systems (CIS) often consist of applications that access shared resources such as databases. Since centralized systems may have a great impact on the system performance, parallel and distributio...
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ISBN:
(纸本)0818683805
Cooperative Information Systems (CIS) often consist of applications that access shared resources such as databases. Since centralized systems may have a great impact on the system performance, parallel and distribution techniques are needed for attaining scalability. Distributed databases are, then, crucial for the development of cooperative applications. However, in order to improve performance, it is very important to design information distribution properly, which is the goal of Distribution Design. Considering the various difficulties embedded in the Design of Distributed Object Oriented Databases, this work presents an algorithm to assist distribution designers in their task. The analysis algorithm indicates the most adequate fragmentation technique (vertical, horizontal or mixed) for each class in the database schema, and we propose the use of a machine learning method - inductive logic programming - to uncover some implicit issues to be considered in the distribution design, thus revising the proposed analysis algorithm.
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 .
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