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Semidefinite programming by perceptron learning
- Advances in Neural Information Processing Systems 16
, 2004
"... We present a modified version of the perceptron learning algorithm (PLA) which solves semidefinite programs (SDPs) in polynomial time. The algorithm is based on the following three observations: (i) Semidefinite programs are linear programs with infinitely many (linear) constraints; (ii) every linea ..."
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Cited by 1 (1 self)
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We present a modified version of the perceptron learning algorithm (PLA) which solves semidefinite programs (SDPs) in polynomial time. The algorithm is based on the following three observations: (i) Semidefinite programs are linear programs with infinitely many (linear) constraints; (ii) every
Perceptron Learning Generalization Bounds for Perceptron Learning
, 2009
"... Why should we look at the Perceptron? • linear classifiers such as perceptrons are the basis of technical neurocomputing • Support Vector Machines are basically linear classifiers • basic concepts of learning theory can be explained easily • margins • slack variables • dual representation • bounds o ..."
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Why should we look at the Perceptron? • linear classifiers such as perceptrons are the basis of technical neurocomputing • Support Vector Machines are basically linear classifiers • basic concepts of learning theory can be explained easily • margins • slack variables • dual representation • bounds
Semidefinite Programming by Perceptron Learning
"... 1 Introduction Semidefinite programming (SDP) is one of the most active research areas in optimisation. Its appeal derives from important applications in combinatorial optimisation and control theory, from the recent development of efficient algorithms for solving SDP problems and the depth and eleg ..."
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and elegance of the underlying optimisation theory [14], which covers linear, quadratic, and second-order cone programming as special cases. Recently, semidefinite programming has been discovered as a useful toolkit in machine learning with applications ranging from pattern separation via ellipsoids [4
Morphological Perceptron Learning
, 1998
"... During the last decade, researchers have applied neural networks to a multitude of difficult tasks which would normally require human intelligence. In particular, perceptrons are used to classify patterns into different classes. Recently, several researchers introduced a novel class of artificial ne ..."
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Cited by 1 (0 self)
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the maximum of the results. We have shown in previous papers that the properties of morphological neural networks differ drastically from those of traditional neural network models. In this paper, we introduce a learning algorithm for multilayer morphological perceptrons which is capable of solving arbitrary
Perceptron learning with random coordinate descent
- California Institute of Technology
, 2005
"... Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. It is perhaps the simplest learning model that is used standalone. In this paper, we propose a family of random coordinate descent algorithms for perceptron learning on binary classification problems. ..."
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Cited by 6 (3 self)
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Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. It is perhaps the simplest learning model that is used standalone. In this paper, we propose a family of random coordinate descent algorithms for perceptron learning on binary classification problems
Conditional convergence of photorefractive perceptron learning
, 1993
"... We consider the convergence characteristics of a perceptron learning algorithm, taking into account the decay of photorefractive holograms during the process of interconnection weight changes. As a result of the hologram erasure, the convergence of the learning process is dependent on the exposure t ..."
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Cited by 3 (3 self)
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We consider the convergence characteristics of a perceptron learning algorithm, taking into account the decay of photorefractive holograms during the process of interconnection weight changes. As a result of the hologram erasure, the convergence of the learning process is dependent on the exposure
A ”Thermal” Perceptron Learning Rule
, 1992
"... The thermal perceptron is a simple extension to Rosenblatt’s percep-tron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, To, has ..."
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Cited by 51 (0 self)
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The thermal perceptron is a simple extension to Rosenblatt’s percep-tron learning rule for training individual linear threshold units. It finds stable weights for nonseparable problems as well as separable ones. Experiments indicate that if a good initial setting for a temperature parameter, To
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