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Dynamic distance-based active learning with SVM 1

引用

5th international conference on machine learning and data mining in pattern recognition, MLDM 2007

作者： Jiang, Jun Ip, Horace H. S. Image Computing Group Department of Computer Science Hong Kong City University of Hong Kong Hong Kong Hong Kong

ISBN: (数字)9783540734994

ISBN: (纸本)9783540734987

In this paper, we present a novel active learning strategy, named dynamic active learning with SVM to improve the effectiveness of learning sample selection in active learning. the algorithm is divided into two steps. the first step is similar to the standard distance-based active learning with SVM [1] in which the sample nearest to the decision boundary is chosen to induce a hyperplane that can halve the current version space. In order to improve upon the learning efficiency and convergent rates, we propose in the second step, a dynamic sample selection strategy that operates within the neighborhood of the "standard" sample. theoretical analysis is given to show that our algorithm will converge faster than the standard distance-based technique and using less number of samples while maintaining the same classification precision rate. We also demonstrate the feasibility of the dynamic selection strategy approach through conducting experiments on several benchmark datasets. © Springer-Verlag Berlin Heidelberg 2007.

关键词： learning algorithms

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ACORNS - towards computational modeling of communication and recognition skills

ACORNS - towards computational modeling of communication and...

引用

IEEE international conference on Cognitive Informatics

作者： Lou Boves Louis ten Bosch Roger Moore Department Language and Speech Radboud University Nijmegen Netherlands Department of Computer Science University of Sheffield UK

In this paper the FP6 Future and Emerging Technologies project ACORNS is introduced. this project aims at simulating embodied language learning, inspired by the memory-prediction theory of intelligence. ACORNS intends to build a full computational implementation of sensory information processing. ACORNS considers linguistic units as emergent patterns. thus, the research will not only address the issues conventionally investigated in statistical pattern recognition, but also the representations that are formed in memory. the paper discusses details of the memory and processing architecture that will be implemented in ACORNS, and explains how this architecture merges the basic concepts of the Memory-Prediction theory with results form previous research in the field of memory.

关键词： Computational modeling Automatic speech recognition pattern recognition Speech recognition Humans Speech processing Natural languages Memory architecture machine learning Training data

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6th IEEE/ACIS international conference on Computer and Information Science-Title

6th IEEE/ACIS International Conference on Computer and Infor...

引用

international conference on Computer and Information Science (ACIS)

the following topics are dealt with: information science; computer architecture; VLSI; image processing; pattern recognition; mobile computing; software engineering; knowledge discovery; agent based systems; data mining; data warehousing; distributed computing; middleware architecture; e commerce; software metrics and speech processing.

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Variable Width Elliptic Gaussian Kernels for Probability Density Estimation

Variable Width Elliptic Gaussian Kernels for Probability Den...

引用

international conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Dragoljub Pokrajac Longin Jan Latecki Aleksandar Lazarevic Jelena Nikolic Computer and Information Science Department Applied Mathematics Research Center and Center for Research and Education in Optical Sciences and Applications (CREOSA) Delaware State University Dover DE USA CIS Department Temple University Philadelphia PA USA CREOSA and United Technologies Research Center Hartford CT USA Faculty of Electronic Engineering University of Nis Nis Serbia

Estimation of probability density functions based on available data is important problem arising in various fields, such as telecommunications, machine learning, data mining, pattern recognition and computer vision. In this paper, we consider Kernel-based non-parametric density estimation methods and derive formulae for variable kernel density estimation using generalized, elliptic Gaussian kernels. the proposed technique is verified on simulated data.

关键词： Kernel Probability density function machine learning data mining pattern recognition Computer vision Eigenvalues and eigenfunctions Random variables State estimation Optical computing

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SVM-Based decision fusion model for detecting concepts in films

SVM-Based decision fusion model for detecting concepts in fi...

引用

international conference on Information, Communications and Signal Processing

作者： P. Muneesawang L. Guan Department of Electrical and Computer Engineering Naresuan University Phitsanulok Thailand Department of Electrical and Computer Engineering Ryerson University Toronto Canada

this paper studies a support vector machine (SVM) to obtain a decision fusion algorithm for detection of semantic concepts in videos, and its application to films database. Given a movie clip, its spatio-temporal information is captured by audiovisual features. these are then independently inputted to the corresponding matching experts whose outputs are fused at the decision stage by the SVM classifier. Based on our simulation results, this fusion method can attain very high recognition accuracy for detection of various concepts from a collection of Hollywood movies. It requires a very small set of training samples from a large database.

关键词： Motion pictures Support vector machines Signal processing algorithms data mining Event detection machine learning Spatial databases Support vector machine classification Information retrieval Indexing

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Smooth SVM research: A polynomial-based approach

Smooth SVM research: A polynomial-based approach

引用

international conference on Information, Communications and Signal Processing

作者： Yan-Feng Fan De-Xian Zhang Hua-Can He College of Information Science and Engineering Henan University of Technology Zhengzhou China College of Computer Science Northwest Polytechnical University Xi'an China

Classification can be promoted by using SVM to acquire hypersurface due to the direct induction of the support vectors. In traditional SVM solution algorithms, objective function is a strictly convex unconstrained optimization problem and it is not differentiable, so it can not use the most used optimization method to solve the problem. the undifferential model could be converted into a differential one by using polynomial to approximate the plus function x + . this paper gives the procedure of using cubic spline interpolation and Hermite interpolation method to deduce the quadratic polynomial smoothing the plus function x + , it also presents a new smooth technology using circular arc polynomial. the proposed approach is experimentally evaluated in three datasets that are benchmarks for data mining applications, leading to interesting results.

关键词： Support vector machines Polynomials Interpolation Support vector machine classification Optimization methods Spline Educational institutions data mining Neural networks pattern recognition

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A Classification Approach of Granules Based on Variable Precision Rough Sets

A Classification Approach of Granules Based on Variable Prec...

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IEEE international conference on Cognitive Informatics

作者： Shen-Ming Gu Wei-Zhi Wu Hong-Tao Chen School of Mathematics Physics and Information Science Zhejiang Ocean University Zhoushan Zhejiang China

the key to granular computing (GrC) is to make use of granules in problem solving. Classification is one of important problems in machine learning and data mining. With view of granular computing, this paper presents a classification approach to granules based on the variable precision rough set (VPRS) model. An algorithm is proposed and a tree structure of granules is given.

关键词： Rough sets Problem-solving data mining Tree data structures Set theory Information systems Mathematics Physics Information science Oceans

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Acoustic Features Extraction for Emotion recognition

Acoustic Features Extraction for Emotion Recognition

引用

international conference on Computer and Information Science (ACIS)

作者： Jia Rong Yi-Ping Phoebe Chen Morshed Chowdhury Gang Li School of Engineering and Information Technology Deakin University Melbourne Australia

In the last decade, the efforts of spoken language processing have achieved significant advances, however, the work with emotional recognition has not progressed so far, and can only achieve 50% to 60% in accuracy. this is because a majority of researchers in this field have focused on the synthesis of emotional speech rather than focusing on automating human emotion recognition. Many research groups have focused on how to improve the performance of the classifier they used for emotion recognition, and few work has been done on data pre-processing, such as the extraction and selection of a set of specifying acoustic features instead of using all the possible ones they had in hand. To work with well-selected acoustic features does not mean to delay the whole job, but this will save much time and resources by removing the irrelative information and reducing the high-dimension data calculation. In this paper, we developed an automatic feature selector based on a RF2TREE algorithm and the traditional C4.5 algorithm. RF2TREE applied here helped us to solve the problems that did not have enough data examples. the ensemble learning technique was applied to enlarge the original data set by building a bagged random forest to generate many virtual examples, and then the new data set was used to train a single decision tree, which selects the most efficient features to represent the speech signals for the emotion recognition. Finally, the output of the selector was a set of specifying acoustic features, produced by RF2TREE and a single decision tree.

关键词： Feature extraction Emotion recognition Psychology machine learning Decision trees Speech synthesis Humans data mining Signal processing algorithms Speech recognition

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A Novel Ensemble Strategy for Classification of Prostate Cancer Protein Mass Spectra

A Novel Ensemble Strategy for Classification of Prostate Can...

引用

29th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.15

作者： Amin Assareh Mohammad Hassan Moradi Vahid Esmaeili Biomedical Engineering Amirkabir University of Technology슠 Tehran Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran

Protein mass spectra pattern recognition is a new forum, in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. the high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision. Using a limited number of biomarkers along with 5 classification algorithms, the proposed method achieved a promising performance over a well-known prostate cancer mass spectroscopy dataset.

关键词： Prostate cancer Proteins machine learning algorithms Proteomics Biomarkers Cancer detection Classification algorithms Mass spectroscopy data mining pattern recognition

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Evaluating learning Algorithms to Construct Rule Evaluation Models Based on Objective Rule Evaluation Indices

Evaluating Learning Algorithms to Construct Rule Evaluation ...

引用

IEEE international conference on Cognitive Informatics

作者： Hidenao Abe Shusaku Tsumoto Miho Ohsaki Takahira Yamaguchi School of Medicine Shimane University Japan Faculty of Engineering Doshisha University Japan Faculty of Science and Technology Keio University Japan

In the present paper, we describe an evaluation of our rule evaluation support method with constructive meta- learning scheme for post-processing of mined results with rule evaluation models based on objective indices. Postprocessing of mined results is one of the key processes in the data mining process. However, it is difficult for human experts to completely evaluate several thousand of rules from a large dataset with noises. To reduce the costs in such a rule evaluation task, we have developed a rule evaluation support method with rule evaluation models, which learn from objective indices for mined classification rules and evaluations by a human expert for each rule. To enhance the adaptability of rule evaluation models, we introduced a constructive meta-learning scheme to choose proper learning algorithms. then, we performed the case study on the meningitis data mining as an actual problem. In addition, we evaluated the proposed method using the ten rule sets obtained from the ten UCI datasets. the obtained results demonstrate the applicability of the proposed rule evaluation support method.

关键词： Humans data mining Cognitive informatics Paper technology Costs Statistical analysis learning systems database systems machine learning algorithms Noise generators

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