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The Nature of Statistical Learning Theory
, 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
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Cited by 12976 (32 self)
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Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based
Provable Algorithms for Machine Learning Problems
, 2013
"... Modern machine learning algorithms can extract useful information from text, images and videos. All these applications involve solving NPhard problems in average case using heuristics. What properties of the input allow it to be solved efficiently? Theoretically analyzing the heuristics is often v ..."
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Cited by 2 (0 self)
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Modern machine learning algorithms can extract useful information from text, images and videos. All these applications involve solving NPhard problems in average case using heuristics. What properties of the input allow it to be solved efficiently? Theoretically analyzing the heuristics is often
HESSIAN FREE OPTIMIZATION METHODS FOR MACHINE LEARNING PROBLEMS
"... Abstract. In this article, we describe the algorithm and study the performance of a Hessian free optimization technique applied to machine learning problems. We implement the commonly used black box model for optimization and solve a particular challenging recursive neural network learning problem, ..."
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Abstract. In this article, we describe the algorithm and study the performance of a Hessian free optimization technique applied to machine learning problems. We implement the commonly used black box model for optimization and solve a particular challenging recursive neural network learning problem
Lexical Acquisition: A Novel Machine Learning Problem
, 1996
"... This paper defines a new machine learning problem to which standard machine learning algorithms cannot easily be applied. The problem occurs in the domain of lexical acquisition. The ambiguous and synonymous nature of words causes the difficulty of using standard induction techniques to learn a lexi ..."
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This paper defines a new machine learning problem to which standard machine learning algorithms cannot easily be applied. The problem occurs in the domain of lexical acquisition. The ambiguous and synonymous nature of words causes the difficulty of using standard induction techniques to learn a
Object correspondence as a machine learning problem
 In Proceedings of the 22nd International Conference on Machine Learning (ICML 05
, 2005
"... We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points of one ..."
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Cited by 15 (5 self)
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We propose machine learning methods for the estimation of deformation fields that transform two given objects into each other, thereby establishing a dense point to point correspondence. The fields are computed using a modified support vector machine containing a penalty enforcing that points
New Functional Representation for the Decomposition of Machine Learning Problems
 Machine Learning Problem”, Third Symposium on Logic Design and Learning, Conference Proceedings
, 2000
"... The central idea in machine learning is to gather information from a given data set. This can be a very difficult task because practical databases are usually very large. To address this difficulty, the assumption of Occam's Razor (simplest is best) and explicit domain knowledge are used to red ..."
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. Plus, it has been shown by [10] that logic synthesis methods (specifically functional decomposition) can be used for finding solutions in the machine learning paradigm. One problem in machine learning is the concept of missing attributes (input don't know). The concept of a missing attribute
Machine learning problems from optimization perspective
"... Abstract Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss thr ..."
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Cited by 3 (2 self)
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Abstract Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss
A Survey of Axiom Selection as a Machine Learning Problem
"... Abstract. Automatic theorem provers struggle to discharge proof obligations of interactive theorem provers. This is partly due to the large number of background facts that are passed to the automatic provers as axioms. Axiom selection algorithms predict the relevance of facts, thereby helping to re ..."
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to reduce the search space of automatic provers. This paper presents an introduction to axiom selection as a machine learning problem and describes the challenges that distinguish it from other applications of machine learning. 1
Gaussian processes for machine learning
 in: Adaptive Computation and Machine Learning
, 2006
"... Abstract. We give a basic introduction to Gaussian Process regression models. We focus on understanding the role of the stochastic process and how it is used to define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperpar ..."
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Cited by 631 (2 self)
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of statistics and machine learning, either for analysis of data sets, or as a subgoal of a more complex problem. Traditionally parametric 1 models have been used for this purpose. These have a possible advantage in ease of interpretability, but for complex data sets, simple parametric models may lack expressive
Ensemble Methods in Machine Learning
 MULTIPLE CLASSIFIER SYSTEMS, LBCS1857
, 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging, and boostin ..."
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Cited by 607 (3 self)
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Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include errorcorrecting output coding, Bagging
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