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Tabu search model selection for SVM

Tabu search model selection for SVM

引用

7th international conference on Intelligent data Engineering and Automated learning (IDEAL 2006)

作者： Lebrun, Gilles Charrier, Christophe Lezoray, Olivier Cardot, Hubert Univ Caen Basse Normandie GREYC UMR 6072 CNRS Image Team F-14050 Caen France Univ Tours Lab Informat EA 2101 F-37200 Tours France

A model selection method based on tabu search is proposed to build support vector machines (binary decision functions) of reduced complexity and efficient generalization. the aim is to build a fast and efficient support vector machines classifier. A criterion is defined to evaluate the decision function quality which blends recognition rate and the complexity of a binary decision functions together. the selection of the simplification level by vector quantization, of a feature subset and of support vector machines hyperparameters are performed by tabu search method to optimize the defined decision function quality criterion in order to find a good sub-optimal model on tractable times.

关键词： model selection metaheuristic tabu search machine learning Support Vector machines vector quantisation pattern recognition data mining

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Self learning and its Implications for machine Understanding

Self Learning and its Implications for Machine Understanding

引用

4th international conference on Information and Automation for Sustainability

作者： Dinalankara, Wikum De Silva, Daswin Alahakoon, Damminda Monash Univ Cognit & Connectionist Syst Lab Clayton Vic 3800 Australia

ISBN: (纸本)9781424428991

Contemporary machine intelligence is far from realizing prominent hallmarks of human understanding and consciousness. the primary shortcoming of current methods can be attributed to the difficulty or implausibility of foreseeing and pre-programming each and every piece of information or knowledge. Emergent intelligence methods based on principles of self learning and self organization have been successful in infusing traits of understanding in machines. this understanding is in contrast to the constrained intelligence permeated on machines by classical approaches of intelligence following supervised knowledge acquisition mechanisms. the primary objective of this paper is to review current work in emergent intelligence methods and discuss means of orchestrating these in to a practical model that resembles the process of human understanding. the paper delineates intricacies of self-learning in humans from both biological and psychological perspectives. Following a discussion of several artificial models of the human mind that have been researched and documented at the conceptual level, we propose a comparatively pragmatic approach based on a novel unsupervised learning algorithm, the GSOM algorithm. this algorithm has been successfully applied to many real world knowledge acquisition and pattern discovery problems. the paper concludes with a further discussion of research developments in emergent systems, which we perceive to be the stepping stones in the search for true machine understanding.

关键词： artificial cognition self learning unsupervised learning growing self organising map algorithm multi-modal data fusion

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A comparative study of machine learning methods for detecting promoters in bacterial DNA sequences

引用

4th international conference on Intelligent Computing, ICIC 2008

作者： Tavares, Leonardo G. Lopes, Heitor S. Erig Lima, Carlos R. 80230-901 Brazil

ISBN: (纸本)3540859837

machine learning methods have been widely used in bioinformatics, mainly for data classification and pattern recognition. the detection of genes in DNA sequences is still an open problem. Identifying the promoter region laying prior the gene itself is an important aid to detect a gene. this paper aims at applying several machine learning methods to the construction of classifiers for detection of promoters in the DNA of Escherichia coli. A thorough comparison of methods was done. In general, probabilistic and neural network-based methods were those that performed better regarding accuracy rate. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Bioinformatics

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Unsupervised Sequential Forward Dimensionality Reduction Based On Fractal

Unsupervised Sequential Forward Dimensionality Reduction Bas...

引用

5th international conference on Fuzzy Systems and Knowledge Discovery

作者： Yan, GuangHui Liu, LiSong Du, LinNa Yang, XiaXia Ma, ZhiCheng Lanzhou Jiaotong Univ Sch Informat & Elect Engn Lanzhou 730070 Peoples R China Gansu Elect Power Informat & Commun Cent Lanzhou 730050 Peoples R China

ISBN: (纸本)9780769533056

Dimensionality reduction has long been an active research topic within statistics, pattern recognition, machine learning and data mining. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. In this paper we transform the attribute selection problem into the optimization problem which tries to find the attribute subset with the maximal fractal dimension and the attribute number restriction simultaneously. In order to avoid exhaustive search in the huge attribute subset space we integrate the individual attribute priority with attribute subset evaluation for dimensionality reduction and propose the unsupervised Sequential Forward Fractal Dimensionality Reduction(SFFDR) algorithm. Our experiments on synthetic and real datasets show that the algorithm proposed can get the satisfied resulting attribute subset with a rather low time complexity.

关键词： data mining

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Artificial Immune recognition System for DNA Microarray data Analysis

Artificial Immune Recognition System for DNA Microarray Data...

引用

4th international conference on Natural Computation (ICNC 2008)

作者： Chen, Chuanhang Xu, Chuan Bie, Rongfang Gao, X. Z. Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Aalto Univ Dept Elect Engn FIN-02150 Espoo Finland

ISBN: (纸本)9780769533049

Artificial Immune Systems (AIS) are emerging information processing methods, which embody the principles of biological immune systems for tackling complex real-world problems. the Artificial Immune recognition System (AIRS) is a new kind of supervised learning AIS. the development of microarray technology has supplied a large volume of data for the prediction and diagnosis of cancer. Many popular machine learning techniques have been used in the microarray data analysis. In this paper, we apply AIRS to perform the microarray data classification based on an improved version of the information gain feature selection method three traditional classifiers have also been employed in our experiments for performance comparison. the results demonstrate the promising ability of AIRS in the microarray data analysis.

关键词： Immune system

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A stagewise least square loss function for classification

A stagewise least square loss function for classification

引用

8th SIAM international conference on data mining 2008, Applied Mathematics 130

作者： Yang, Shuang-Hong Hu, Bao-Gang National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9781605603179

this paper presents a stagewise least square (SLS) loss function for classification. It uses a least square form within each stage to approximate a bounded monotonic nonconvex loss function in a stagewise manner. Several benefits are obtained from using the SLS loss function, such as: (i) higher generalization accuracy and better scalability than classical least square loss;(ii) improved performance and robustness than convex loss (e.g., hinge loss of SVM);(iii) computational advantages compared with nonconvex loss (e.g. ramp loss in ψ-learning);(iv) ability to resist myopia of Empirical Risk Minimization and to boost the margin without boosting the complexity of the classifier. In addition, it naturally results in a kernel machine which is as sparse as SVM, yet much faster and simpler to train. A fast online learning algorithm with an integrated sparsification procedure is also provided. Experimental results on several benchmarks confirm the advantages of the proposed approach.

关键词： Least squares approximations

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Ensemble Implementations on Diversified Support Vector machines

Ensemble Implementations on Diversified Support Vector Machi...

引用

4th international conference on Natural Computation (ICNC 2008)

作者： Li, Kunlun Dai, Yunna Zhang, Wei Hebei Univ Coll Elect & Informat Engn Tianjin 071002 Peoples R China

ISBN: (纸本)9780769533049

Support Vector machine (SVM) is an effective algorithm in pattern recognition. But usually, standard SVM requires solving a quadratic program (QP) problem. In majority situations, most implementations of SVM are approximate solution to the QP problem. As the approximate solutions cannot achieve the expected performance of SRM theory, it is necessary to research ensemble methods for SVM. Recently, in order to augment the diversities of individual classifiers of SVM, many researchers use random partition with the whole training to form sub-training sets. therefore the performance of aggregated SVM, which was trained on those subsets, was improved. We proposed the ensemble method based on different implementations of SVM, because they have large diversities by their different implementing methods. the experiment results showed that this method is effectively to improve the aggregated learner's performance.

关键词： Support vector machines Support vector machine classification Educational institutions Quadratic programming machine learning Equations Diversity reception Artificial neural networks Voting Space technology

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Plant Species Identification Based on Neural Network

Plant Species Identification Based on Neural Network

引用

4th international conference on Natural Computation (ICNC 2008)

作者： Zhang, Lei Kong, Jun Zeng, Xiaoyun Ren, Jiayue Northeast Normal Univ Network Informat Ctr Changchun Jilin Province Peoples R China Northeast Normal Univ Comp Sch Changchun Jilin Province Peoples R China Yangzhou Polytech Coll Yangzhou Jiangsu Peoples R China Aviat Univ AF Changchun Peoples R China

ISBN: (纸本)9780769533049

Computer-Aided Plant Species Identification acts significantly on plant digital museum system and systematic botany, which is the groundwork for research and development of plant. this paper presents a new method for plant species identification using leaf image. It focuses on the stable features extraction of leaf such as the geometrical features of shape and the texture features of venation. the 2-D moment invariants, Wavelet statistical features are used to extract leaf information. Self-Organizing Feature Map (SOM) neural network has the advantages of simple structure, ordered mapping topology and low complexity of learning. It is suitable for many complex problems such as multi-class pattern recognition, high dimension input vector and large quantity training data. So this paper use SOM neural network to identify the plant species. the experimental results illustrate the effectiveness of this method.

关键词： Neural networks Feature extraction Shape Computer networks data mining Artificial neural networks Military computing Educational institutions Research and development Network topology

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Dynamic Growing Self-organizing Neural Network for Clustering

Dynamic Growing Self-organizing Neural Network for Clusterin...

引用

4th international conference on Advanced data mining and Applications

作者： Tian, Daxin Ren, Yueou Li, Qiuju Tianjin Univ Sch Comp Sci & Technol Tianjin 300072 Peoples R China Armor Tech Inst PLA Dept Elect Engn Beijing 130117 Peoples R China

ISBN: (纸本)9783540881919

Neural Networks have been widely used in the field of intelligent information processing such as classification, clustering, prediction, and recognition. Unsupervised learning is the main method to collect and find features from large unlabeled data. In this paper a new unsupervised learning clustering neuron network-Dynamic Growing Self-organizing Neuron Network (DGSNN) is presented. It uses a new competitive learning rule-Improved Winner-Take-All (IWTA) and adds new neurons when it is necessary. the advantage of DGSNN is that it overcomes the usual problems of other clustering methods;dead units and prior knowledge of the number of clusters. In the experiments, DGSNN is applied to clustering tasks to check its ability and is compared with other clustering algorithms RPCL and WTA. the results show that DGSNN performs accurately and efficiently.

关键词： Unsupervised learning

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Self-Organizing Map-Based Fault Dictionary Application Research on Rolling Bearing Faults

Self-Organizing Map-Based Fault Dictionary Application Resea...

引用

4th international conference on Natural Computation (ICNC 2008)

作者： Pi, Jun Lin, Jiaquan Li, Xiangjiang Civil Aviat Univ China Aeronaut Mech Coll Tianjin 300300 Peoples R China Changsha Sci & Technol Univ Sch Comp & Commun Engn Changsha 410076 Hunan Peoples R China

ISBN: (纸本)9780769533049

Vibration signal resulting from rolling bearing defects presents a rich content of physical information the appropriate analysis methods of which can lead to the clear identification of the nature of the fault. A novel procedure is presented for construction offault diagnosis dictionary through Self-organization Map(SOM). the experiments show that the bearing faults diagnosis dictionary could be effectively applied in the vibration pattern recognition for a roller bearing system.

关键词： Dictionaries Rolling bearings Testing Fault diagnosis Ball bearings Educational institutions Vibrations data mining Neurons Feature extraction

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