204 research outputs found
Linear Time Feature Selection for Regularized Least-Squares
We propose a novel algorithm for greedy forward feature selection for
regularized least-squares (RLS) regression and classification, also known as
the least-squares support vector machine or ridge regression. The algorithm,
which we call greedy RLS, starts from the empty feature set, and on each
iteration adds the feature whose addition provides the best leave-one-out
cross-validation performance. Our method is considerably faster than the
previously proposed ones, since its time complexity is linear in the number of
training examples, the number of features in the original data set, and the
desired size of the set of selected features. Therefore, as a side effect we
obtain a new training algorithm for learning sparse linear RLS predictors which
can be used for large scale learning. This speed is possible due to matrix
calculus based short-cuts for leave-one-out and feature addition. We
experimentally demonstrate the scalability of our algorithm and its ability to
find good quality feature sets.Comment: 17 pages, 15 figure
A kernel-based framework for learning graded relations from data
Driven by a large number of potential applications in areas like
bioinformatics, information retrieval and social network analysis, the problem
setting of inferring relations between pairs of data objects has recently been
investigated quite intensively in the machine learning community. To this end,
current approaches typically consider datasets containing crisp relations, so
that standard classification methods can be adopted. However, relations between
objects like similarities and preferences are often expressed in a graded
manner in real-world applications. A general kernel-based framework for
learning relations from data is introduced here. It extends existing approaches
because both crisp and graded relations are considered, and it unifies existing
approaches because different types of graded relations can be modeled,
including symmetric and reciprocal relations. This framework establishes
important links between recent developments in fuzzy set theory and machine
learning. Its usefulness is demonstrated through various experiments on
synthetic and real-world data.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
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Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
We study the adaptation of Link Grammar Parser to the biomedical sublanguage
with a focus on domain terms not found in a general parser lexicon. Using two
biomedical corpora, we implement and evaluate three approaches to addressing
unknown words: automatic lexicon expansion, the use of morphological clues, and
disambiguation using a part-of-speech tagger. We evaluate each approach
separately for its effect on parsing performance and consider combinations of
these approaches. In addition to a 45% increase in parsing efficiency, we find
that the best approach, incorporating information from a domain part-of-speech
tagger, offers a statistically signicant 10% relative decrease in error. The
adapted parser is available under an open-source license at
http://www.it.utu.fi/biolg
Multi-Label Learning under Feature Extraction Budgets
We consider the problem of learning sparse linear models for multi-label prediction tasks under a hard constraint on the number of features. Such budget constraints are important in domains where the acquisition of the feature values is costly. We propose a greedy multi-label regularized least-squares algorithm that solves this problem by combining greedy forward selection search with a cross-validation based selection criterion in order to choose, which features to include in the model. We present a highly efficient algorithm for implementing this procedure with linear time and space complexities. This is achieved through the use of matrix update formulas for speeding up feature addition and cross-validation computations. Experimentally, we demonstrate that the approach allows finding sparse accurate predictors on a wide range of benchmark problems, typically outperforming the multi-task lasso baseline method when the budget is small.</p
Coarticulation in sign and speech
Proceedings of the NODALIDA 2009 workshop
Multimodal Communication — from Human Behaviour to Computational Models.
Editors: Costanza Navarretta, Patrizia Paggio, Jens Allwood,
Elisabeth Alsén and Yasuhiro Katagiri.
NEALT Proceedings Series, Vol. 6 (2009), 21-24.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9208
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
BACKGROUND: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very efficient in general data mining tasks. We explore their capability for the gene versus protein name disambiguation task. RESULTS: We incorporated into the conventional SVM a weighting scheme based on distances of context words from the word to be disambiguated. This weighting scheme increased the performance of SVMs by five percentage points giving performance better than 85% as measured by the area under ROC curve and outperformed the Weighted Additive Classifier, which also incorporates the weighting, and the Naive Bayes classifier. CONCLUSION: We show that the performance of SVMs can be improved by the proposed weighting scheme. Furthermore, our results suggest that in this study the increase of the classification performance due to the weighting is greater than that obtained by selecting the underlying classifier or the kernel part of the SVM
Parsing Clinical Finnish: Experiments with Rule-Based and Statistical Dependency Parsers
Proceedings of the 17th Nordic Conference of Computational Linguistics
NODALIDA 2009.
Editors: Kristiina Jokinen and Eckhard Bick.
NEALT Proceedings Series, Vol. 4 (2009), 65-72.
© 2009 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/9206
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