Results 1 - 10 of 118

Experiments with a New Boosting Algorithm

by Yoav Freund, Robert E. Schapire , 1996
"... In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced the relate ..."
Abstract - Cited by 2213 (20 self) - Add to MetaCart
the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real

Experiments with a New Boosting Algorithm

by unknown authors
"... Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced ..."
Abstract - Add to MetaCart
the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real

Experiments with a New Boosting Algorithm

by unknown authors
"... Abstract. In an earlier paper, we introduced a new “boosting” algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that consistently generates classifiers whose performance is a little better than random guessing. We also introduced ..."
Abstract - Add to MetaCart
the related notion of a “pseudo-loss ” which is a method for forcing a learning algorithm of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real

Active learning strategies for multi-label text classification

by Andrea Esuli, Fabrizio Sebastiani - In Proceedings of the 31st European Conference on Information Retrieval (ECIR 2009 , 2009
"... Abstract. Active learning refers to the task of devising a ranking function that, given a classifier trained from relatively few training examples, ranks a set of additional unlabeled examples in terms of how much further information they would carry, once manually labeled, for retraining a (hopeful ..."
Abstract - Cited by 18 (4 self) - Add to MetaCart
(hopefully) better classifier. Research on active learning in text classification has so far concentrated on single-label classification; active learning for multi-label classification, instead, has either been tackled in a simulated (and, we contend, non-realistic) way, or neglected tout court

Local Rademacher Complexity for Multi-label Learning Local Rademacher Complexity for Multi-label Learning

by Chang Xu, Tongliang Liu, Dacheng Tao, Chao Xu
"... We analyze the local Rademacher complexity of empirical risk minimization (ERM)-based multi-label learning algorithms, and in doing so propose a new algorithm for multi-label learning. Rather than using the trace norm to regularize the multi-label predictor, we instead minimize the tail sum of the s ..."
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of the singular values of the predictor in multi-label learning. Benefiting from the use of the local Rademacher complexity, our algorithm, therefore, has a sharper generalization error bound and a faster convergence rate. Compared to methods that minimize over all singular values, concentrating on the tail

Generalized Multi-Protocol Label Switching (GMPLS) enabled

by Marc Ruiz, Jordi Perelló, Luis Velasco, Salvatore Spadaro, Jaume Comellas
"... Abstract—This paper concentrates on the resilience of the ..."
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Abstract—This paper concentrates on the resilience of the

Unsupervised and semi-supervised multi-class support vector machines

by Linli Xu, Dale Schuurmans - In AAAI-05, The Twentieth National Conference on Artificial Intelligence , 2005
"... We present new unsupervised and semi-supervised training algorithms for multi-class support vector machines based on semidefinite programming. Although support vector machines (SVMs) have been a dominant machine learning technique for the past decade, they have generally been applied to supervised l ..."
Abstract - Cited by 70 (5 self) - Add to MetaCart
on the resulting training data. The problem is hard, but semidefinite relaxations can approximate this objective surprisingly well. While previous work has concentrated on the two-class case, we present a general, multi-class formulation that can be applied to a wider range of natural data sets. The resulting

Technological progress and sustainable development: what about the rebound effect?

by Mathias Binswanger - Ecol. Econ. , 2001
"... Abstract Sustainability concepts that rest on the idea of resource-or energy-efficiency improvements due to technological progress tend to overestimate the potential saving effects because they frequently ignore the behavioral responses evoked by technological improvements. Efficiency improvements ..."
Abstract - Cited by 70 (1 self) - Add to MetaCart
also affect the demand for resources and energy, and often an increase in efficiency by 1% will cause a reduction in resource use that is far below 1% or, sometimes, it can even cause an increase in resource use. This phenomenon is commonly labeled the rebound effect, which is well-known among energy

Documentation of IANA assignments for Generalized MultiProtocol Label Switching (GMPLS) Resource Reservation Protocol- Traffic Engineering (RSVP-TE)

by Z. Lin, D. Pendarakis, Extensions For , 2003
"... This memo provides information for the Internet community. It does not specify an Internet standard of any kind. Distribution of this memo is unlimited. Copyright Notice Copyright (C) The Internet Society (2003). All Rights Reserved. The Generalized MultiProtocol Label Switching (GMPLS) suite of pro ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
This memo provides information for the Internet community. It does not specify an Internet standard of any kind. Distribution of this memo is unlimited. Copyright Notice Copyright (C) The Internet Society (2003). All Rights Reserved. The Generalized MultiProtocol Label Switching (GMPLS) suite

Dose-Response of Superparamagnetic Iron Oxide Labeling on Mesenchymal Stem Cells Chondrogenic Differentiation: A Multi-Scale In Vitro Study

by Emilie Roeder, Christel Henrionnet, Jean Christophe Goebel, Nicolas Gambier, Olivier Beuf, Denis Grenier, Bailiang Chen, Pierre-andre ́ Vuissoz, Pierre Gillet, Astrid Pinzano
"... Aim: The aim of this work was the development of successful cell therapy techniques for cartilage engineering. This will depend on the ability to monitor non-invasively transplanted cells, especially mesenchymal stem cells (MSCs) that are promising candidates to regenerate damaged tissues. Methods: ..."
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: MSCs were labeled with superparamagnetic iron oxide particles (SPIO). We examined the effects of long-term labeling, possible toxicological consequences and the possible influence of progressive concentrations of SPIO on chondrogenic differentiation capacity. Results: No influence of various SPIO
Results 1 - 10 of 118