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371
Classifying and solving horn clauses for verification
 In VSTTE
, 2013
"... Abstract. As a promising direction to overcome difficulties of verification, researchers have recently proposed the use of Horn constraints as intermediate representation. Horn constraints are related to Craig interpolation, which is one of the main techniques used to construct and refine abstractio ..."
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Cited by 6 (2 self)
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abstractions in verification, and to synthesise inductive loop invariants. We give a classification of the different forms of Craig interpolation problems found in literature, and show that all of them correspond to natural fragments of (recursionfree) Horn constraints. For a logic that has the binary
First steps in programming: A rationale for attention investment models.
 In Proc. HCC, IEEE
, 2002
"... Abstract Research into the cognitive aspects of programming originated in the study of professional programmers (whether experts or students). Even "enduser" programmers What is Programming? Goodell's excellent website devoted to end user programming Programming is in fact seld ..."
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Cited by 110 (16 self)
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task at home. We have proposed elsewhere a system suitable for enhancing domestic remote controls with programming abilities In order to avoid these inconsistencies, the first proposal of this paper is that all computer users ought to be regarded as potential programmers, whose tools differ only
Learning valued preference structures for solving classification problems
 Fuzzy Sets and Systems
"... This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our approach first decomposes a polychotomous classification problem involving m classes into an ensemble of binary problems ..."
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Cited by 14 (3 self)
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This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our approach first decomposes a polychotomous classification problem involving m classes into an ensemble of binary
A Fuzzy Logic Classifier for Transient Stability Assessment
, 2000
"... Transient stability assessment (TSA) of a power system pursues a twofold objective: first to appraise the system’s capability to withstand major contingencies and second to suggest remedial actions whenever needed. The first objective is the concern of analysis; the second is a matter of control. T ..."
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system for TSA. The fuzzy logicbased rule assessment needs only a fraction of time to solve the classification problem, namely to classify an operating point of the machine as a stable or unstable one. The results revealed that the proposed classifier system is flexible and extendible.
Applications of SAT solving
, 2003
"... In the area of formal verification it is well known that there can be no single logic that suits all needs. This insight motivates the diversity of this dissertation: it contains contributions to SAT solving, First Order theorem proving and Model Finding, and Symbolic Model Checking. A growing numb ..."
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In the area of formal verification it is well known that there can be no single logic that suits all needs. This insight motivates the diversity of this dissertation: it contains contributions to SAT solving, First Order theorem proving and Model Finding, and Symbolic Model Checking. A growing
A Higher Order Collective Classifier for Detecting and Classifying Network Events
"... Abstract—Labeled Data is scarce. Most statistical machine learning techniques rely on the availability of a large labeled corpus for building robust models for prediction and classification. In this paper we present a Higher Order Collective Classifier (HOCC) based on Higher Order Learning, a statis ..."
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Abstract—Labeled Data is scarce. Most statistical machine learning techniques rely on the availability of a large labeled corpus for building robust models for prediction and classification. In this paper we present a Higher Order Collective Classifier (HOCC) based on Higher Order Learning, a
Constraint Inductive Logic Programming
, 1996
"... . This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A discrim ..."
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Cited by 22 (6 self)
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. This paper is concerned with learning from positive and negative examples expressed in firstorder logic with numerical constants. The presented approach is based on the cooperation of Inductive Logic Programming (ILP) and Constraint Logic Programming (CLP), and proceeds as follows: ffl A
A Study of Approaches to Hypertext Categorization
 Journal of Intelligent Information Systems
, 2002
"... . Hypertext poses new research challenges for text classification. Hyperlinks, HTML tags, category labels distributed over linked documents, and meta data extracted from related web sites all provide rich information for classifying hypertext documents. How to appropriately represent that informatio ..."
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Cited by 116 (4 self)
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, and whose presence (or absence) may significantly influence the optimal design of a classifier. Using three hypertext datasets and three wellknown learning algorithms (Naive Bayes, Nearest Neighbor, and First Order Inductive Learner), we examine these regularities in different domains, and compare
Lagrangian Support Vector Machines
, 2000
"... An implicit Lagrangian for the dual of a simple reformulation of the standard quadratic program of a linear support vector machine is proposed. This leads to the minimization of an unconstrained differentiable convex function in a space of dimensionality equal to the number of classified points. Thi ..."
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Cited by 110 (11 self)
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in 11 lines of MATLAB code without any special optimization tools such as linear or quadratic programming solvers. This LSVM code can be used "as is" to solve classification problems with millions of points. For example, 2 million points in 10 dimensional input space were classified by a
Data Selection for Support Vector Machine Classifiers
 In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
, 2000
"... The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave min imization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest n ..."
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Cited by 31 (4 self)
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The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classifier, is formulated as a concave min imization problem and solved by a finite number of linear programs. This minimal set of data points, which is the smallest
Results 1  10
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371