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检索条件"任意字段=First International Workshop on Deterministic and Statistical Methods in Machine Learning"

共 69 条记录，以下是61-70 订阅

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Reports on the Twenty-first National Conference on Artificial Intelligence (AAAI-06) workshop program

Reports on the Twenty-First National Conference on Artificia...

引用

作者： Achtner, Wolfgang Aimeur, Esma Anand, Sarabjot Singh Appelt, Doug Ashish, Naveen Barnes, Tiffany Beck, Joseph E. Beetz, Michael Dias, M. Bernardine Doshi, Prashant Drummond, Chris Elazmeh, William Felner, Ariel Freitag, Dayne Geffner, Hector Geib, Christopher W. Goodwin, Richard Holte, Robert C. Hutter, Frank Isaac, Fair Japkowicz, Nathalie Kaminka, Gal A. Koenig, Sven Lagoudakis, Michail G. Leake, David Lewis, Lundy Liu, Hugo Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Poupart, Pascal Pynadath, David V. Rajan, Kanna Roth-Berghofer, Thomas Ruml, Wheeler Rusu, Radu Bogdan Schulz, Stefan Schwarz, Sven Seneff, Stephanie Sheth, Amit Sun, Ron Thielscher, Michael Upal, Afzal Williams, Jason Young, Steve Zelenko, Dmitry LSDIS Lab. University of Georgia Athens GA United States IBM T. J. Watson Research Center Carnegie Mellon Unversity Pittsburgh PA United States University of Southern California Los Angeles CA United States Technical University of Crete Chania Greece University of Toledo Toledo OH United States Rensselaer Poytechnic Institute Troy NY 12180 United States Universität München München Germany Monterey Bay Aquarium Research Institute Moss Landing CA United States Dresden University of Technology Dresden Germany Technische Universität München München Germany Massachusetts Institute of Technology Cambridge MA United States University of North Texas Denton TX United States University of Montreal Montreal QC Canada University of North Carolina at Charlotte Charlotte NC United States NRS Institute for Information Technology University of Ottawa Ottawa ON Canada University of California Irvine Irvine CA United States SRI International SRA Ben-Gurion University of Negev University of Alberta Edmonton AB Canada UPF Wimberly School of Religion Southern New Hampshire University Manchester NH United States Justus-Liebig Universität Giessen DePaul University Chicago IL United States University of Warwick Coventry United Kingdom Palo Alto Research Center Palo Alto CA United States University of British Columbia Vancouver BC Canada TU Kaiserslautern Indiana University Bloomington IN United States The e-Spirit Company GmbH DFKI GmbH Bar Ilan University Tel Aviv Israel ISI University of Southern California Los Angeles CA United States Honeywell Labs University of Waterloo Waterloo ON Canada AT and T Labs-Research Cambridge University Cambridge United Kingdom

The workshop program of the Twenty first National Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AI-Driven Technologies for Service-Oriented Computing;Auction Mechanisms for Robot Coordination;Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics;Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness;Educational Data Mining;Evaluation methods for machine learning;Event Extraction and Synthesis;Heuristic Search, Memory-Based Heuristics, and Their Applications;Human Implications of Human-Robot Interaction;Intelligent Techniques in Web Personalization;learning for Search;Modeling and Retrieval of Context;Modeling Others from Observations;and statistical and Empirical Approaches for Spoken Dialogue Systems. Copyright © 2006, American Association for Artificial Intelligence. All rights reserved.

关键词： Artificial intelligence

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Appropriate kernel functions for support vector machine learning with sequences of symbolic data

Appropriate kernel functions for support vector machine lear...

引用

1st international workshop on deterministic and statistical methods in machine learning

作者： Vanschoenwinkel, B Manderick, B Free Univ Brussels Computat Modeling Lab B-1050 Brussels Belgium

ISBN: (纸本)3540290737

In classification problems, machine learning algorithms often make use of the assumption that (dis)similar inputs lead to (dis)similar outputs. In this case, two questions naturally arise: what does it mean for two inputs to be similar and how can this be used in a learning algorithm? In support vector machines, similarity between input examples is implicitly expressed by a kernel function that calculates inner products in the feature space. For numerical input examples the concept of an inner product is easy to define, for discrete structures like sequences of symbolic data however these concepts are less obvious. This article describes an approach to SVM learning for symbolic data that can serve as an alternative to the bag-of-words approach under certain circumstances. This latter approach first transforms symbolic data to vectors of numerical data which are then used as arguments for one of the standard kernel functions. In contrast, we will propose kernels that operate on the symbolic data directly.

关键词： Functions

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SVM based learning system for Information Extraction

SVM based learning system for Information Extraction

引用

1st international workshop on deterministic and statistical methods in machine learning

作者： Li, YY Bontcheva, K Cunningham, H Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

ISBN: (纸本)3540290737

This paper presents an SVM-based learning system for information extraction (IE). One distinctive feature of our system is the use of a variant of the SVM, the SVM with uneven margins, which is particularly helpful for small training datasets. In addition, our approach needs fewer SVM classifiers to be trained than other recent SVM-based systems. The paper also compares our approach to several state-of-the-art systems (including rule learning and statistical learning algorithms) on three IE benchmark datasets: CoNLL-2003, CMU seminars, and the software jobs corpus. The experimental results show that our system outperforms a recent SVM-based system on CoNLL-2003, achieves the highest score on eight out of 17 categories on the Jobs corpus, and is second best on the remaining nine.

关键词： learning systems

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deterministic and statistical methods in machine learning 2005

引用

丛书名： Lecture Notes in Computer Science

2005年

作者： Joab Winkler Neil Lawrence Mahesan Niranjan

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Kernel based learning methods:: Regularization networks and RBF networks

Kernel based learning methods:: Regularization networks and ...

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1st international workshop on deterministic and statistical methods in machine learning

作者： Kudová, P Neruda, R Acad Sci Czech Republ Inst Comp Sci Prague 18207 Czech Republic

ISBN: (纸本)3540290737

We discuss two kernel based learning methods, namely the Regularization Networks (RN) and the Radial Basis Function (RBF) Networks. The RNs are derived from the regularization theory, they had been studied thoroughly from a function approximation point of view, and they posses a sound theoretical background. The RBF networks represent a model of artificial neural networks with both neuro-physiological and mathematical motivation. In addition they may be treated as a generalized form of Regularization Networks. We demonstrate the performance of both approaches on experiments, including both benchmark and real-life learning tasks. We claim that RN and RBF networks are comparable in terms of generalization error, but they differ with respect to their model complexity. The RN approach usually leads to solutions with higher number of base units, thus, the RBF networks can be used as a 'cheaper' alternative. This allows to utilize the RBF networks in modeling tasks with large amounts of data, such as time series prediction or semantic web classification.

关键词： learning systems

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Integrating binding site predictions using non-linear classification methods

Integrating binding site predictions using non-linear classi...

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1st international workshop on deterministic and statistical methods in machine learning

作者： Sun, Y Robinson, M Adams, R Kaye, P Rust, A Davey, N Univ Hertfordshire Sci & Technol Res Sch Hatfield AL10 9AB Herts England Inst Syst Biol Seattle WA 98103 USA

ISBN: (纸本)3540290737

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key algorithms. Furthermore, we use a 'window' of consecutive results in the input vector in order to contextualise the neighbouring results. Moreover, we improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines outperform each of the original individual algorithms and other classifiers employed in this work with both type of inputs, in that they maintain a better tradeoff between recall and precision.

关键词： Classification (of information)

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Bayesian kernel learning methods for parametric accelerated life survival analysis

Bayesian kernel learning methods for parametric accelerated ...

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first international workshop on deterministic and statistical methods in machine learning

作者： Cawley, Gavin C. Talbot, Nicola L. C. Janacek, Gareth J. Peck, Michael W. School of Computing Sciences University of East Anglia Norwich NR4 7TJ United Kingdom Institute of Food Research Norwich Research Park Norwich NR4 7UA United Kingdom

ISBN: (纸本)3540290737

Survival analysis is a branch of statistics concerned with the time elapsing before "failure", with diverse applications in medical statistics and the analysis of the reliability of electrical or mechanical components. In this paper we introduce a parametric accelerated life survival analysis model based on kernel learning methods that, at least in principal, is able to learn arbitrary dependencies between a vector of explanatory variables and the scale of the distribution of survival times. The proposed kernel survival analysis method is then used to model the growth domain of Clostridium botulinum, that is the food processing and storage conditions permitting the growth of this foodborne microbial pathogen, leading to the production of the neurotoxin responsible for botulism. A Bayesian training procedure, based on the evidence framework, is used for model selection and to provide a credible interval on model predictions. The kernel survival analysis models are found to be more accurate than models based on more traditional survival analysis techniques, but also suggest a risk assessment of the foodborne botulism hazard would benefit from the collection of additional data. © Springer-Verlag Berlin Heidelberg 2005.

关键词： learning systems

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Joint IAPR international workshops on Structural, Syntactic Pattern Recognition and statistical Techniques in Pattern Recognition, SSPR 2000 and SPR 2000

Joint IAPR International Workshops on Structural, Syntactic ...

引用

international Association of Pattern Recognition, IAPR 2000 held jointly with 8th international workshop on Structural and Syntactic Pattern Recognition, SSPR 2000 and 3rd international workshop on statistical Techniques in Pattern Recognition, SPR 2000

ISBN: (纸本)3540679464

The proceedings contain 92 papers. The special focus in this conference is on Pierre Devijver Lecture and Hybrid and Combined methods. The topics include: Some notes on twenty one 21 nearest prototype classifiers;adaptive graphical pattern recognition beyond connectionist-based approaches;current trends in grammatical inference;classifier's complexity control while training multilayer perceptrons;a framework for classifier fusion;image pattern recognition based on examples;a hybrid system for the recognition of hand-written characters;improving statistical measures of feature subsets by conventional and evolutionary approaches;selection of classifiers based on multiple classifier behaviour;the adaptive subspace map for image description and image database retrieval;adaptative automatic target recognition with SVM boosting for outlier detection;a multiresolution causal colour texture model;writer identification;syntactic pattern recognition by error correcting analysis on tree automata;offline recognition of syntax-constrained cursive handwritten text;structural classification for retrospective conversion of documents;segmentation of date field on bank cheques;grammars and discourse theory to describe and recognize mechanical assemblies;computation of the N best parse trees for weighted and stochastic context-free grammars;partitional vs hierarchical clustering using a minimum grammar complexity approach;encoding nondeterministic finite-state tree automata in sigmoid recursive neural networks;a structural matching algorithm using generalized deterministic annealing;alignment and correspondence using singular value decomposition;fast candidate elimination using machine learning techniques and efficient alignment and correspondence using edit distance.

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Advances in predictive data mining methods 1st

Advances in predictive data mining methods

引用

1st international workshop on machine learning and Data Mining in Pattern Recognition, MLDM 1999

作者： Hong, Se June Weiss, Sholom M. IBM T.J. Watson Research Center P.O. Box 218 Yorktown HeightsNY10598 United States

ISBN: (纸本)3540665994

Predictive models have been widely used long before the development of the new field that we call data mining. Expanding application demand for data mining of ever increasing data warehouses, and the need for understandability of predictive models with increased accuracy of prediction, all have fueled recent advances in automated predictive methods. We first examine a few successful application areas and technical challenges they present. We discuss some theoretical developments in PAC learning and statistical learning theory leading to the emergence of support vector machines. We then examine some technical advances made in enhancing the performance of the models both in accuracy (boosting, bagging, stacking) and scalability of modeling through distributed model generation. © Springer-Verlag Berlin Heidelberg 1999.

关键词： Data mining

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