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2,639
Large margin methods for structured and interdependent output variables
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2005
"... Learning general functional dependencies between arbitrary input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representations, this paper addresses the complementary ..."
Abstract
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Cited by 624 (12 self)
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to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulation. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm
Large Margin Classification Using the Perceptron Algorithm
- Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
Abstract
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Cited by 521 (2 self)
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We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leave-one-out method. Like Vapnik 's maximal-margin classifier, our algorithm takes advantage of data that are linearly separable
Distance metric learning for large margin nearest neighbor classification
- In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
Abstract
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Cited by 695 (14 self)
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We show how to learn a Mahanalobis distance metric for k-nearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin
An extensive empirical study of feature selection metrics for text classification
- J. of Machine Learning Research
, 2003
"... Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison ..."
Abstract
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Cited by 496 (15 self)
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in different situations. The results reveal that a new feature selection metric we call ‘Bi-Normal Separation ’ (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly
SAMPLE-SEPARATION-MARGIN BASED MINIMUM CLASSIFICATION ERROR TRAINING OF PATTERN CLASSIFIERS WITH QUADRATIC DISCRIMINANT FUNCTIONS
"... In this paper, we present a new approach to minimum classification error (MCE) training of pattern classifiers with quadratic discriminant functions. First, a so-called sample separation margin (SSM) is defined for each training sample and then used to define the misclassification measure in MCE for ..."
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In this paper, we present a new approach to minimum classification error (MCE) training of pattern classifiers with quadratic discriminant functions. First, a so-called sample separation margin (SSM) is defined for each training sample and then used to define the misclassification measure in MCE
Governance Matters III: Governance Indicators for 1996–2002.” Policy Research Working Paper 3106
, 2003
"... Kaufmann, Kraay, and Mastruzzi present estimates of six aggregate governance indicators in each of the four dimensions of governance covering 199 countries and periods. They present the point estimates of the territories for four time periods: 1996, 1998, 2000, and dimensions of governance as well a ..."
Abstract
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Cited by 298 (1 self)
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as the margins of 2002. These indicators are based on several hundred errors for each country for the four periods. The individual variables measuring perceptions of governance indicators reported here are an update and governance, drawn from 25 separate data sources expansion of previous research work
Graph embedding and extension: A general framework for dimensionality reduction
- IEEE TRANS. PATTERN ANAL. MACH. INTELL
, 2007
"... Over the past few decades, a large family of algorithms—supervised or unsupervised; stemming from statistics or geometry theory—has been designed to provide different solutions to the problem of dimensionality reduction. Despite the different motivations of these algorithms, we present in this paper ..."
Abstract
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Cited by 271 (29 self)
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connects the marginal points and characterizes the interclass separability. We show that MFA effectively overcomes the limitations of the traditional Linear Discriminant Analysis algorithm due to data distribution assumptions and available projection directions. Real face recognition experiments show
At the Margins:
"... Children. He has worked in Asia and Europe on children’s and young people’s rights, protection, participation, research, and evaluation. He formerly held permanent and honorary positions in British universities, and before that ran an award-winning rights, advice, and counseling project in the UK. H ..."
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for assisting street children will be discussed in a separate paper, “A Guide for Staff: Working with Street Children ” which is currently being prepared. Copyright: Asian Development Bank 2003 All rights reserved. The views expressed in this book are those of the author and do not necessarily reflect the views
Taxes and the location of production: evidence from a panel of US multinationals
- Journal of Public Economics
, 1998
"... This paper considers the factors that influence the locational decisions of multinational firms. A model in which firms produce differentiated products in imperfectly competitive markets is developed, in the spirit of Horstmann and Markusen (1992). Firms choose between a number of foreign locations; ..."
Abstract
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Cited by 227 (24 self)
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; the outside options of exporting to or not serving the foreign market are explicitly modelled. Particular attention is paid to the impact of profit taxes; the separate roles of effective average and marginal tax rates are identified. The model is applied to a panel of US firms locating in the European market
Support vector machines: Training and applications
- A.I. MEMO 1602, MIT A. I. LAB
, 1997
"... The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT&T Bell Laboratories [3, 6, 8, 24]. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perc ..."
Abstract
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Cited by 223 (3 self)
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-Layer Perceptron classifiers. The main idea behind the technique is to separate the classes with a surface that maximizes the margin between them. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle [23]. The derivation
Results 1 - 10
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2,639
