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4th international Multi-conference on Computing in the Global Information Technology

作者： Forsati, Rana Rahbar, Afsaneh Mahdavi, Mehrdad Islamic Azad Univ Dept Comp Engn Karaj Branch Karaj Iran Islamic Azad Univ Dept Comp Engn North Tehran Branch Tehran Iran Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424446803

Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose an algorithm to solve the web page recommendation problem. In our algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned pattern. Our experiments on real data set show that the proposed algorithm performs better than the other algorithms that we compared to and, at the same time, it is less complex than other algorithms with respect to memory usage and computational cost too.

关键词： Personalization machine learning learning Automata Web Mining

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Mining Bilingual data from the Web with Adaptively Learnt patterns

Mining Bilingual Data from the Web with Adaptively Learnt Pa...

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Joint conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th international Joint conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Jiang, Long Yang, Shiquan Zhou, Ming Liu, Xiaohua Zhu, Qingsheng Microsoft Research Asia Beijing100190 China Chongqing University Chongqing400044 China

ISBN: (纸本)9781617382581

Mining bilingual data (including bilingual sentences and terms1) from the Web can benefit many NLP applications, such as machine translation and cross language information retrieval. In this paper, based on the observation that bilingual data in many web pages appear collectively following similar patterns, an adaptive pattern-based bilingual data mining method is proposed. Specifically, given a web page, the method contains four steps: 1) preprocessing: parse the web page into a DOM tree and segment the inner text of each node into snippets;2) seed mining: identify potential translation pairs (seeds) using a word based alignment model which takes both translation and transliteration into consideration;3) pattern learning: learn generalized patterns with the identified seeds;4) pattern based mining: extract all bilingual data in the page using the learned patterns. Our experiments on Chinese web pages produced more than 7.5 million pairs of bilingual sentences and more than 5 million pairs of bilingual terms, both with over 80% accuracy. © 2009 ACL and AFNLP.

关键词： Websites

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Class Prediction from Disparate Biological data Sources Using an Iterative Multi-Kernel Algorithm

Class Prediction from Disparate Biological Data Sources Usin...

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4th international conference pattern recognition in Bioinformatics

作者： Ying, Yiming Cambell, Colin Damoulas, theodoros Girolami, Mark Univ Bristol Dept Engn Math Bristol BS8 1TR Avon England Univ Glasgow Dept Comp Sci Glasgow G12 8QQ Lanark Scotland

ISBN: (纸本)9783642040306

For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. Ail established approach to pursuing supervised learning with multiple types of data, is to encode these different types of data into separate kernels and use multiple kernel learning. In this paper we propose a, simple iterative approach to multiple kernel learning (MKL), focusing on multi-class classification. this approach uses a block L-1-regularization term leading to a jointly convex formulation. It solves a standard multi-class classification problem for a single kernel, and then updates the kernel combinatorial coefficients based oil mixed RKHS norms. As opposed to other MKL approaches, our iterative approach delivers a largely ignored message that MKL does not require sophisticated optimization methods while keeping competitive training times and accuracy across a variety of problems. We show that the proposed method outperforms state-of-the-art results oil all important protein fold prediction dataset and gives competitive performance on a protein subcellular localization task.

关键词： Multiple kernel learning multi-class bioinformatics protein fold prediction protein subcellular localization

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Voted NER system using appropriate unlabeled data

Voted NER system using appropriate unlabeled data

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2009 Named Entities Workshop: Shared Task on Transliteration, NEWS 2009 at the Joint conference of the 47th Annual Meeting of the ACL and the 4th international Joint conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009

作者： Ekbal, Asif Bandyopadhyay, Sivaji Dept. of Computer Science and Engg. Jadavpur University Kolkata700032 India

this paper reports a voted Named Entity recognition (NER) system with the use of appropriate unlabeled data. the proposed method is based on the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector machine (SVM) and has been tested for Bengali. the system makes use of the language independent features in the form of different contextual and orthographic word level features along with the language dependent features extracted from the Part of Speech (POS) tagger and gazetteers. Context patterns generated from the unlabeled data using an active learning method have been used as the features in each of the classifiers. A semi-supervised method has been used to describe the measures to automatically select effective documents and sentences from unlabeled data. Finally, the models have been combined together into a final system by weighted voting technique. Experimental results show the effectiveness of the proposed approach with the overall Recall, Precision, and F-Score values of 93.81%, 92.18% and 92.98%, respectively. We have shown how the language dependent features can improve the system performance. © 2009 ACL and AFNLP.

关键词： Support vector machines

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Exploiting unsupervised and supervised classification for segmentation of the pathological lung in CT

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JOURNAL OF INSTRUMENTATION 2009年第7期4卷 P07013-P07013页

作者： Korfiatis, P. Kalogeropoulou, C. Daoussis, D. Petsas, T. Adonopoulos, A. Costaridou, L. Univ Patras Sch Med Dept Med Phys Patras 26500 Greece Univ Hosp Patras Dept Radiol Patras 26500 Greece Univ Hosp Patras Dept Rheumatol Patras 26500 Greece

Delineation of lung fields in presence of diffuse lung diseases (DLPDs), such as interstitial pneumonias (IP), challenges segmentation algorithms. To deal with IP patterns affecting the lung border an automated image texture classification scheme is proposed. the proposed segmentation scheme is based on supervised texture classification between lung tissue ( normal and abnormal) and surrounding tissue ( pleura and thoracic wall) in the lung border region. this region is coarsely defined around an initial estimate of lung border, provided by means of Markov Radom Field modeling and morphological operations. Subsequently, a support vector machine classifier was trained to distinguish between the above two classes of tissue, using textural feature of gray scale and wavelet domains. 17 patients diagnosed with IP, secondary to connective tissue diseases were examined. Segmentation performance in terms of overlap was 0.924 +/- 0.021, and for shape differentiation mean, rms and maximum distance were 1.663 +/- 0.816, 2.334 +/- 1.574 and 8.0515 +/- 6.549 mm, respectively. An accurate, automated scheme is proposed for segmenting abnormal lung fields in HRC affected by IP

关键词： data processing methods pattern recognition, cluster finding, calibration and fitting methods Computerized Tomography (CT) and Computed Radiography (CR)

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machine learning framework for classification in medicine and biology

Machine learning framework for classification in medicine an...

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6th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR 2009

作者： Lee, Eva K. Center for Operations Research in Medicine and HealthCare School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA 30332-0205 United States

ISBN: (纸本)3642019285

Systems modeling and quantitative analysis of large amounts of complex clinical and biological data may help to identify discriminatory patterns that can uncover health risks, detect early disease formation, monitor treatment and prognosis, and predict treatment outcome. In this talk, we describe a machine-learning framework for classification in medicine and biology. It consists of a pattern recognition module, a feature selection module, and a classification modeler and solver. the pattern recognition module involves automatic image analysis, genomic pattern recognition, and spectrum pattern extractions. the feature selection module consists of a combinatorial selection algorithm where discriminatory patterns are extracted from among a large set of pattern attributes. these modules are wrapped around the classification modeler and solver into a machine learning framework. the classification modeler and solver consist of novel optimization-based predictive models that maximize the correct classification while constraining the inter-group misclassifications. the classification/predictive models 1) have the ability to classify any number of distinct groups;2) allow incorporation of heterogeneous, and continuous/time-dependent types of attributes as input;3) utilize a high-dimensional data transformation that minimizes noise and errors in biological and clinical data;4) incorporate a reserved-judgement region that provides a safeguard against over-training;and 5) have successive multi-stage classification capability. Successful applications of our model to developing rules for gene silencing in cancer cells, predicting the immunity of vaccines, identifying the cognitive status of individuals, and predicting metabolite concentrations in humans will be discussed. We acknowledge our clinical/biological collaborators: Dr. Vertino (Winship Cancer Institute, Emory), Drs. Pulendran and Ahmed (Emory Vaccine Center), Dr. Levey (Neurodegenerative Disease and Alzheimer's Disease

关键词： Forecasting

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thalamic bursts mediate pattern recognition

Thalamic bursts mediate pattern recognition

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Magnus Jandel Swedish Defence Research Agency Stockholm Sweden

A new functional model for burst firing in the dorsal thalamus is proposed where thalamocortical pattern recognition systems, based on kernel machine principles, are connected by burst signaling. the systems include input trapping in the dorsal thalamus, cortical learning state memory and processing in the thalamic reticular nucleus. Misclassified events are captured as training examples in the waking state and the pattern recognition systems are trained by extensive thalamic bursting in deep sleep.

关键词： pattern recognition Support vector machines Support vector machine classification Kernel Registers Neurons Digital relays Mathematical model Neural engineering machine learning

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On Utilizing Optimal and Information theoretic Syntactic Modeling for Peptide Classification

On Utilizing Optimal and Information Theoretic Syntactic Mod...

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4th international conference pattern recognition in Bioinformatics

作者： Aygun, Eser Oommen, B. John Cataltepe, Zehra Istanbul Tech Univ Dept Comp Eng TR-80626 Istanbul Turkey Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada Univ Agder Kristiansand Norway Istanbul Tech Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9783642040306

Syntactic methods in pattern recognition have been used extensively in bioinformatics, and in particular, in the analysis of gene and protein expressions, and in the recognition and classification of biosequences, these methods are almost universally distance-based. this paper concerns the use of an Optimal and Information theoretic (OIT) probabilistic model [11] to achieve peptide classification using the information residing in their syntactic representations. the latter has traditionally been achieved using the edit distances required in the respective peptide comparisons. We advocate that, one can model the differences between compared strings as a mutation model consisting of random Substitutions, Insertions and Deletions (SID) obeying the OIT model. thus, in this paper, we show that the probability measure obtained. from the OIT model can be perceived as a sequence similarity metric, using which a Support Vector machine (SVM)-based peptide classifier, referred to as OIT-SVM, can be devised. the classifier, which we have built has been tested for eight different "substitution" matrices and for two different data sets, namely, the HIV-1 Protease Cleavage sites and the T-cell Epitopes. the results show that the OIT model performs significantly better than the one which uses a Needleman-Wunsch sequence alignment score, and the peptide classification methods that previously experimented with the same two datasets.

关键词： Biological Sequence Analysis Optimal and Information theoretic Syntactic Classifcation Peptide Classification Sequence Processing Syntactic pattern recognition

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Static hand sign recognition using linear projection methods

Static hand sign recognition using linear projection methods

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international conference on Autonomous Robots and Agents, ICARA

作者： Nuwan Gamage Kuang Ye Chow Rini Akmeliawati Department of Electrical & Computer Systems Engineering Monash University Bandar Sunway Malaysia Department of Mechatronics Engineering International Islamic University Malaysia Gombak Malaysia

Shape matching is one of the more significant research topics in the fields of computer vision, pattern recognition and machine learning. Successful shape matching algorithms/ methods has a high potential for a wide variety of practical applications. In this paper, we present our effort on using linear projection methods for static hand sign recognition in Malaysian sign language. PCA and LPP methods have been used with a database of 240 hand shapes.

关键词： Shape Handicapped aids pattern matching Computer vision pattern recognition machine learning machine learning algorithms Application software Principal component analysis databases

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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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