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2,752
The CN2 Induction Algorithm
 MACHINE LEARNING
, 1989
"... Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple, comprehensib ..."
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Cited by 890 (6 self)
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Systems for inducing concept descriptions from examples are valuable tools for assisting in the task of knowledge acquisition for expert systems. This paper presents a description and empirical evaluation of a new induction system, cn2, designed for the efficient induction of simple
Markov Logic Networks
 MACHINE LEARNING
, 2006
"... We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the ..."
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Cited by 816 (39 self)
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We propose a simple approach to combining firstorder logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a firstorder knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects
Learning Stochastic Logic Programs
, 2000
"... Stochastic Logic Programs (SLPs) have been shown to be a generalisation of Hidden Markov Models (HMMs), stochastic contextfree grammars, and directed Bayes' nets. A stochastic logic program consists of a set of labelled clauses p:C where p is in the interval [0,1] and C is a firstorder r ..."
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Cited by 1194 (81 self)
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order rangerestricted definite clause. This paper summarises the syntax, distributional semantics and proof techniques for SLPs and then discusses how a standard Inductive Logic Programming (ILP) system, Progol, has been modied to support learning of SLPs. The resulting system 1) nds an SLP with uniform
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
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Cited by 440 (17 self)
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In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability
A simple inductive synthesis methodology and its applications
, 2010
"... Given a highlevel specification and a lowlevel programming language, our goal is to automatically synthesize an efficient program that meets the specification. In this paper, we present a new algorithmic methodology for inductive synthesis that allows us to do this. We use Second Order logic as ou ..."
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Cited by 16 (9 self)
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Given a highlevel specification and a lowlevel programming language, our goal is to automatically synthesize an efficient program that meets the specification. In this paper, we present a new algorithmic methodology for inductive synthesis that allows us to do this. We use Second Order logic
Detecting intrusion using system calls: alternative data models
 In Proceedings of the IEEE Symposium on Security and Privacy
, 1999
"... Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. In this paper we study one such observable— sequences of system calls into the kernel of an operating system. Using systemcall data sets generated by several differen ..."
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Cited by 433 (3 self)
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different programs, we compare the ability of different data modeling methods to represent normal behavior accurately and to recognize intrusions. We compare the following methods: Simple enumeration of observed sequences, comparison of relative frequencies of different sequences, a rule induction technique
A simple inductive measure analysis for cardinals under the Axiom of Determinacy
 THE PROCEEDINGS OF THE NORTH TEXAS LOGIC CONFERENCE; ILLC PUBLICATION SERIES
"... In this paper, we give a thorough and basic introduction to the main techniques dealing with computation of cardinals under the Axiom of Determinacy by measure analyses. As an application, we give a simple inductive measure analysis (without invoking Jackson’s “description theory”) that allows the ..."
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Cited by 2 (2 self)
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In this paper, we give a thorough and basic introduction to the main techniques dealing with computation of cardinals under the Axiom of Determinacy by measure analyses. As an application, we give a simple inductive measure analysis (without invoking Jackson’s “description theory”) that allows
On the Classification of Simple Inductive Limit C∗Algebras, I: The Reduction Theorem
 DOCUMENTA MATH.
, 2002
"... Suppose that A = lim n→ ∞ (An = tn⊕ i=1 M [n,i](C(Xn,i)), φn,m) is a simple C ∗algebra, where Xn,i are compact metrizable spaces of uniformly bounded dimensions (this restriction can be relaxed to a condition of very slow dimension growth). It is proved in this article that A can be written as an i ..."
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Cited by 43 (5 self)
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Suppose that A = lim n→ ∞ (An = tn⊕ i=1 M [n,i](C(Xn,i)), φn,m) is a simple C ∗algebra, where Xn,i are compact metrizable spaces of uniformly bounded dimensions (this restriction can be relaxed to a condition of very slow dimension growth). It is proved in this article that A can be written
The Use of Explicit Plans to Guide Inductive Proofs
 9TH CONFERENCE ON AUTOMATED DEDUCTION
, 1988
"... We propose the use of explicit proof plans to guide the search for a proof in automatic theorem proving. By representing proof plans as the specifications of LCFlike tactics, [Gordon et al 79], and by recording these specifications in a sorted metalogic, we are able to reason about the conjectures ..."
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Cited by 295 (40 self)
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by building a proof plan based on a simple subset of the implicit proof plan embedded in the BoyerMoore theorem prover, [Boyer & Moore 79].
Modal Languages And Bounded Fragments Of Predicate Logic
, 1996
"... Model Theory. These are nonempty families I of partial isomorphisms between models M and N , closed under taking restrictions to smaller domains, and satisfying the usual BackandForth properties for extension with objects on either side  restricted to apply only to partial isomorphisms of size ..."
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Cited by 273 (12 self)
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are preserved under partial isomorphism, by a simple induction. More precisely, one proves, for any assignment A and any partial isomorphism IÎI which is defined on the Avalues for all variables x 1 , ..., x k , that M, A = f iff N , IoA = f . The crucial step in the induction is the quantifier case
Results 1  10
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2,752