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6th international conference on machine learning and data mining in pattern recognition

作者： Gad, Walaa K. Kamel, Mohamed S. Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9783642030697

Most text clustering techniques are based on words and/or phrases weights in the text. Such representation is often unsatisfactory because it ignores the relationships between terms, and considers them as independent features. In this paper, a new semantic similarity based model (SSBM) is proposed. The semantic similarity based model computes semantic similarities by utilizing WordNet as an ontology. The proposed model captures the semantic similarities between documents that contain semantically similar terms but unnecessarily syntactically identical. The semantic similarity based model assigns a new weight to document terms reflecting the semantic relationships between terms that co-occur literally in the document. Our model in conjunction with the extended gloss overlaps measure and the adapted Lesk algorithm solves ambiguity, synonymy problems that are not detected using traditional term frequency based text milling techniques. The proposed model is evaluated on the Reuters-21578 and the 20-Newsgroups text collections datasets. The performance is assessed in terms of the Fmeasure, Purity and Entropy quality measures. The obtained results show promising performance improvements compared to the traditional term based vector space model (VSM) as well as other existing methods that include semantic similarity measures in text clustering.

关键词： Gloss overlaps measure semantic similarity measures WordNet word sense disambiguation text clustering

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Assessing the Eligibility of Kidney Transplant Donors

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6th international conference on machine learning and data mining in pattern recognition

作者： Reinaldo, Francisco Fernandes, Carlos Rahman, Md Anishur Malucelli, Andreia Camacho, Rui Univ Porto FEUP Rua Dr Roberto FriasSn P-4200465 Porto Portugal Bairro Univ UnilesteMG Ctr Univ Leste Minas Gerais GIC BR-3571056 Coronel Fabriciano MG Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-215901 Curitiba Parana Brazil Pontificia Univ Catolica Parana PUCPR PostGrad Programme Hlth Technol PPGTS BR-80215901 Curitiba Parana Brazil Univ Porto FEUP Porto 4200465 Portugal

ISBN: (纸本)9783642030697

Organ transplantation is a highly complex decision process that requires expert, decisions. The major problem ill a transplantation procedure is the possibility of the receiver's immune system attack and destroy the transplanted tissue. It is therefore of capital importance to find a donor with the highest possible compatibility with the receiver, and thus reduce rejection. Finding a good donor is not a straightforward task because a complex network of relations exist's between the immunological and the clinical variables that, influence the receivers acceptance of the transplanted organ. Currently the process of analyzing these variables involves a careful study by the clinical transplant team. The number and complexity of the relations between variables make the manual process very slow. Ill this paper we propose and compare two machine learning algorithms that might help the transplant team ill improving and Speeding up their decisions. We achieve that objective by analyzing past real cases and constructing models as set, of rules. Such models are accurate and understandable by experts.

关键词： learning algorithms

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learning betting tips from users' bet selections

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6th international conference on machine learning and data mining in pattern recognition, MLDM 2009

作者： Štrumbelj, Erik Šikonja, Marko Robnik Kononenko, Igor Faculty of Computer and Information Science University of Ljubljana Trzaška 25 Ljubljana 1000 Slovenia

ISBN: (纸本)3642030696

In this paper we address the problem of using bet selections of a large number of mostly non-expert users to improve sports betting tips. A similarity based approach is used to describe individual users' strategies and we propose two different scoring functions to evaluate them. The information contained in users' bet selections improves on using only bookmaker odds. Even when only bookmaker odds are used, the approach gives results comparable to those of a regression-based forecasting model. © 2009 Springer Berlin Heidelberg.

关键词： data mining

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Advances in Intelligent data Analysis VIII 2009

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丛书名： Lecture Notes in Computer Science

2009年

作者： Niall M. Adams Céline Robardet Arno Siebes Jean-Francois Boulicaut

ISBN: (数字)9783642039157

ISBN: (纸本)9783642039140

This book constitutes the refereed proceedings of the 8th international conference on Intelligent data Analysis, IDA 2009, held in Lyon, France, August 31 September 2, 2009. The 33 revised papers, 18 full oral presentations and 15 poster and short oral presentations, presented were carefully reviewed and selected from almost 80 submissions. All current aspects of this interdisciplinary field are addressed; for example interactive tools to guide and support data analysis in complex scenarios, increasing availability of automatically collected data, tools that intelligently support and assist human analysts, how to control clustering results and isotonic classification trees. In general the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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Sequential em for unsupervised adaptive gaussian mixture model based classifier

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6th international conference on machine learning and data mining in pattern recognition, MLDM 2009

作者： Awwad Shiekh Hasan, Bashar Gan, John Q. School of Computer Science and Electronic Engineering University of Essex Wivenhoe Park CO4 3SQ United Kingdom

ISBN: (纸本)3642030696

In this paper we present a sequential expectation maximization algorithm to adapt in an unsupervised manner a Gaussian mixture model for a classification problem. The goal is to adapt the Gaussian mixture model to cope with the non-stationarity in the data to classify and hence preserve the classification accuracy. Experimental results on synthetic data show that this method is able to learn the time-varying statistical features in data by adapting a Gaussian mixture model online. In order to control the adaptation method and to ensure the stability of the adapted model, we introduce an index to detect when the adaptation would fail. © 2009 Springer Berlin Heidelberg.

关键词： Maximum principle

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Targeting Sentiment Expressions through Supervised Ranking of Linguistic Configurations 3

Targeting Sentiment Expressions through Supervised Ranking o...

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3rd international AAAI conference on Weblogs and Social Media, ICWSM 2009

作者： Kessler, Jason S. Nicolov, Nicolas Dept. of Computer Science Indiana University BloomingtonIN47405 United States J.D. Power and Associates McGraw-Hill 4888 Pearl East Circle BoulderCO80301 United States

ISBN: (纸本)9781577354215

User generated content is extremely valuable for mining market intelligence because it is unsolicited. We study the problem of analyzing users' sentiment and opinion in their blog, message board, etc. posts with respect to topics expressed as a search query. In the scenario we consider the matches of the search query terms are expanded through coreference and meronymy to produce a set of mentions. The mentions are contextually evaluated for sentiment and their scores are aggregated (using a data structure we introduce call the sentiment propagation graph) to produce an aggregate score for the input entity. An extremely crucial part in the contextual evaluation of individual mentions is finding which sentiment expressions are semantically related to (target) which mentions - this is the focus of our paper. We present an approach where potential target mentions for a sentiment expression are ranked using supervised machine learning (Support Vector machines) where the main features are the syntactic configurations (typed dependency paths) connecting the sentiment expression and the mention. We have created a large English corpus of product discussions blogs annotated with semantic types of mentions, coreference, meronymy and sentiment targets. The corpus proves that coreference and meronymy are not marginal phenomena but are really central to determining the overall sentiment for the top-level entity. We evaluate a number of techniques for sentiment targeting and present results which we believe push the current state-of-the-art. Copyright © 2009, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Supervised learning

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SentiFul: Generating a reliable lexicon for sentiment analysis

SentiFul: Generating a reliable lexicon for sentiment analys...

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international conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Alena Neviarouskaya Helmut Prendinger Mitsuru Ishizuka University of Tokyo Bunkyo Tokyo Japan National Institute of Information Chiyoda Tokyo Japan

The main drawback of any lexicon-based sentiment analysis system is the lack of scalability. Thus, in this paper, we will describe methods to automatically generate and score a new sentiment lexicon, called SentiFul, and expand it through direct synonymy relations and morphologic modifications with known lexical units. We propose to distinguish four types of affixes (used to derive new words) depending on the role they play with regard to sentiment features: propagating, reversing, intensifying, and weakening.

关键词： data mining Spatial databases Tagging Mutual information Informatics Scalability Target recognition machine learning Clustering algorithms machine learning algorithms

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Towards user-independent classification of multimodal emotional signals

Towards user-independent classification of multimodal emotio...

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international conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Jonghwa Kim Elisabeth André Thurid Vogt Augsburg University Augsburg Germany

Coping with differences in the expression of emotions is a challenging task not only for a machine, but also for humans. Since individualism in the expression of emotions may occur at various stages of the emotion generation process, human beings may react quite differently to the same stimulus. Consequently, it comes as no surprise that recognition rates reported for a user-dependent system are significantly higher than recognition rates for a user-independent system. Based on empirical data we obtained in our earlier work on the recognition of emotions from biosignals, speech and their combination, we discuss which consequences arise from individual user differences for automated recognition systems and outline how these systems could be adapted to particular user groups.

关键词： Emotion recognition Humans Psychology Automatic speech recognition Appraisal System testing Speech analysis pattern recognition machine learning algorithms Audio recording

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learning with a quadruped chopstick robot

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6th international conference on machine learning and data mining in pattern recognition, MLDM 2009

作者： Lee, Wei-Chung Chen, Jong-Chen Wu, Shou-Zhe Lin, Kuo-Ming National Yunlin University of Science and Technology Taiwan

ISBN: (纸本)3642030696

Organisms exhibit a close structure-function relationship and a slight change in structure may in turn change their outputs accordingly [1]. This feature is important as it is the main reason why organisms have better malleability than computers in dealing with environmental changes. A quadruped chopstick robot controlled by a biologically-motivated neuromolecular model, named Miky, has been developed. Miky's skeleton and its four feet were comprised of 16 deposable chopsticks, with each foot being controlled by an actuator (motor). The neuromolecular model is a multilevel neural network which captures the biological structure-function relationship and serves to transform signals sent from its sensors into a sequence of signals in space and time for controlling Miky's feet (through actuators). The task is to teach Miky to walk, jump, pace, gallop, or make a turn. Our experimental result shows that Miky exhibits a close structure-function relationship that allows it to learn to accomplish these tasks in a continuous manner. © 2009 Springer Berlin Heidelberg.

关键词： Robots

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Enabling multi-level relevance feedback on pubmed by integrating rank learning into DBMS

Enabling multi-level relevance feedback on pubmed by integra...

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3rd ACM international Workshop on data and Text mining in Bioinformatics, DTMBIO'09, Co-located with the 18th ACM international conference on Information and Knowledge Management, CIKM 2009

作者： Yu, Hwanjo Kim, Taehoon Oh, Jinoh Ko, Ilhwan Kim, Sungchul Han, Wook-Shin CSE Department POSTECH Pohang Korea Republic of CE Department Kyungpook National University Daegu Korea Republic of

ISBN: (纸本)9781605588032

Background: Finding relevant articles from PubMed is challenging because it is hard to express the user's specific intention in the given query interface, and a keyword query typically retrieves a large number of results. Researchers have applied machine learning techniques to find relevant articles by ranking the articles according to the learned relevance function. However, the process of learning and ranking is usually done offline without integrated with the keyword queries, and the users have to provide a large amount of training documents to get a reasonable learning accuracy. This paper proposes a novel multi-level relevance feedback system for PubMed, called RefMed, which supports both ad-hoc keyword queries and a multi-level relevance feedback in real time on PubMed. Results: RefMed supports a multi-level relevance feedback by using the RankSVM as the learning method, and thus it achieves higher accuracy with less feedback. RefMed "tightly" integrates the RankSVM into rdBMS to support both keyword queries and the multi-level relevance feedback in real time;the tight coupling of the RankSVM and DBMS substantially improves the processing time. An efficient parameter selection method for the RankSVM is also proposed, which tunes the RankSVM parameter without performing validation. Thereby, RefMed achieves a high learning accuracy in real time without performing a validation process. RefMed is accessible at {\sf http://***/refmed}. Conclusions: RefMed is the first multi-level relevance feedback system for PubMed, which achieves a high accuracy with less feedback. It effectively learns an accurate relevance function from the user's feedback and efficiently processes the function to return relevant articles in real time. Copyright 2009 ACM.

关键词： learning systems

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