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Results 1 - 10
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16,670
On combining classifiers
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental ..."
Abstract
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Cited by 1420 (33 self)
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We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision
Simplification by cooperating decision procedures
- ACM Transactions on Programming Languages and Systems
, 1979
"... A method for combining decision procedures for several theories into a single decision procedure for their combination is described, and a simplifier based on this method is discussed. The simplifier finds a normal form for any expression formed from individual variables, the usual Boolean connectiv ..."
Abstract
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Cited by 455 (2 self)
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A method for combining decision procedures for several theories into a single decision procedure for their combination is described, and a simplifier based on this method is discussed. The simplifier finds a normal form for any expression formed from individual variables, the usual Boolean
Combining decision procedures
- In Formal Methods at the Cross Roads: From Panacea to Foundational Support, Lecture Notes in Computer Science
, 2003
"... Abstract. We give a detailed survey of the Nelson-Oppen method for combining decision procedures, we show how Shostak's method can be seen as an instance of the Nelson-Oppen method, and we provide a generalization of the Nelson-Oppen method to the case of non-disjoint theories. 1 Introduction D ..."
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Cited by 15 (1 self)
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Abstract. We give a detailed survey of the Nelson-Oppen method for combining decision procedures, we show how Shostak's method can be seen as an instance of the Nelson-Oppen method, and we provide a generalization of the Nelson-Oppen method to the case of non-disjoint theories. 1 Introduction
Strategies for Combining Decision Procedures
, 2003
"... Implementing efficient algorithms for combining decision procedures has been a challenge and their correctness precarious. In this paper we describe an inference system that has the classical Nelson-Oppen procedure at its core and includes several optimizations: variable abstraction with sharing, ca ..."
Abstract
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Cited by 10 (2 self)
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Implementing efficient algorithms for combining decision procedures has been a challenge and their correctness precarious. In this paper we describe an inference system that has the classical Nelson-Oppen procedure at its core and includes several optimizations: variable abstraction with sharing
Strategies for combining decision procedures �
"... www.elsevier.com/locate/tcs Implementing efficient algorithms for combining decision procedures has been a challenge and their correctness precarious. In this paper we describe an inference system that has the classical Nelson–Oppen procedure at its core and includes several optimizations: variable ..."
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www.elsevier.com/locate/tcs Implementing efficient algorithms for combining decision procedures has been a challenge and their correctness precarious. In this paper we describe an inference system that has the classical Nelson–Oppen procedure at its core and includes several optimizations: variable
Combining decision procedures for the reals
- In preparation
"... Vol. 2 (4:4) 2006, pp. 1–42 www.lmcs-online.org ..."
Decision Combination in Multiple Classifier Systems
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 16. NO. I. JANUARY 1994
, 1994
"... A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings of ..."
Abstract
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Cited by 377 (5 self)
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A multiple classifier system is a powerful solution to difficult pattern recognition problems involving large class sets and noisy input because it allows simultaneous use of arbitrary feature descriptors and classification procedures. Decisions by the classifiers can be represented as rankings
A training algorithm for optimal margin classifiers
- PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
Abstract
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Cited by 1865 (43 self)
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is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC
Exploiting Generative Models in Discriminative Classifiers
- In Advances in Neural Information Processing Systems 11
, 1998
"... Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often resu ..."
Abstract
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Cited by 551 (9 self)
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Generative probability models such as hidden Markov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand, discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often
Combined decision procedures . . .
, 2011
"... We describe contributions to algorithmic proof techniques for deciding the satisfiability of boolean combinations of many-variable nonlinear polynomial equations and inequalities over the real and complex numbers. In the first half, we present an abstract theory of Gröbner basis construction algorit ..."
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We describe contributions to algorithmic proof techniques for deciding the satisfiability of boolean combinations of many-variable nonlinear polynomial equations and inequalities over the real and complex numbers. In the first half, we present an abstract theory of Gröbner basis construction
Results 1 - 10
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16,670
