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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 13236 (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
Challenges in statistical machine learning
 Statistica Sinica
"... Machine learning and statistics are one and the same discipline, with different communities of researchers attacking essentially the same fundamental problems from different perspectives. In this note we briefly describe some current challenges in the field of statistical machine learning that cu ..."
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Cited by 4 (0 self)
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Machine learning and statistics are one and the same discipline, with different communities of researchers attacking essentially the same fundamental problems from different perspectives. In this note we briefly describe some current challenges in the field of statistical machine learning
Statistical Machine Learning for Information Retrieval
 School of Computer Science, Carnegie Mellon Univ
, 2001
"... The purpose of this work is to introduce and experimentally validate a framework, based on statistical machine learning, for handling a broad range of problems in information retrieval (IR). ..."
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Cited by 6 (0 self)
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The purpose of this work is to introduce and experimentally validate a framework, based on statistical machine learning, for handling a broad range of problems in information retrieval (IR).
Riemannian geometry and statistical machine learning
, 2005
"... Statistical machine learning algorithms deal with the problem of selecting an appropriate statistical model from a model space Θ based on a training set {xi} N i=1 ⊂ X or {(xi, yi)} N i=1 ⊂ X × Y. In doing so they either implicitly or explicitly make assumptions on the geometries of the model space ..."
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Cited by 16 (4 self)
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Statistical machine learning algorithms deal with the problem of selecting an appropriate statistical model from a model space Θ based on a training set {xi} N i=1 ⊂ X or {(xi, yi)} N i=1 ⊂ X × Y. In doing so they either implicitly or explicitly make assumptions on the geometries of the model space
Statistical Machine Learning and Combinatorial Optimization
 Theoretical Aspects of Evolutionary Computing
, 2000
"... In this work we apply statistical learning methods in the context of combinatorial optimization, which is understood as nding a binary string minimizing a given cost function. We rst consider probability densities over binary strings and we dene two dierent statistical criteria. Then we recast t ..."
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Cited by 12 (1 self)
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In this work we apply statistical learning methods in the context of combinatorial optimization, which is understood as nding a binary string minimizing a given cost function. We rst consider probability densities over binary strings and we dene two dierent statistical criteria. Then we recast
Querying the Web with Statistical Machine Learning
"... Abstract The traditional means of extracting information from the Web are keywordbased search and browsing. The Semantic Web adds structured information (i.e., semantic annotations and references) supporting both activities. One of the most interesting recent developments is Linked Open Data (LOD) w ..."
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Cited by 1 (1 self)
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, by analyzing and exploiting statistical regularities. We discuss challenges when applying machine learning to the Web and discuss the particular learning approaches we have been pursuing in THESEUS. We discuss a number of applications, where the Web is queried via machine learning and describe several
Statistical Machine Learning Group, Canberra
, 2009
"... Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have many variants such as NMF, PLSI and LDA, and are used in many fields such as genetics, text and the web, image analysis and recommen ..."
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Topic models are a discrete analogue to principle component analysis and independent component analysis that model topic at the word level within a document. They have many variants such as NMF, PLSI and LDA, and are used in many fields such as genetics, text and the web, image analysis and recommender systems. However, only recently have reasonable methods for estimating the likelihood of unseen documents, for instance to perform testing or model comparison, become available. This paper explores a number of recent methods, and improves their theory, performance, and testing.
Results 1  10
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54,726