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Applications of average geodesic distance in manifold learning

引用

3rd international conference on Rough Sets and Knowledge Technology, RSKT 2008

作者： Zeng, Xianhua School of Computer Science China West Normal University Nanchong 637002 China

ISBN: (纸本)3540797203

Manifold learning has become a hot issue in the research fields of machine learning and data mining. Current manifold learning algorithms assume that the observed data set has the high density. But, how to evaluate the denseness of the high dimensional observed data set? This paper proposes an algorithm based on the average geodesic distance as the preprocessing step of manifold learning. Moreover, for a high dense data set evaluated, we further utilize the average geodesic distance to quantitatively analyze the mapping relationship between the high-dimensional manifold and the corresponding intrinsic low-dimensional manifold in the known ISOMAP algorithm. Finally, experimental results on two synthetic Swiss-roll data sets show that our method is feasible. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： learning systems

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Named entity recognition for South Asian languages 3

Named entity recognition for South Asian languages

引用

IJCNLP Workshop on Named Entity recognition for South and South East Asian Languages, held in conjunction with the 3rd international Joint conference on Natural Language Processing, IJCNLP 2008

作者： Goyal, Amit University of Utah School of Computing Salt Lake CityUT United States

Much work has already been done on building named entity recognition systems. However most of this work has been concentrated on English and other European languages. Hence, building a named entity recognition (NER) system for South Asian Languages (SAL) is still an open problem because they exhibit characteristics different from English. This paper builds a named entity recognizer which also identifies nested name entities for the Hindi language using machine learning algorithm, trained on an annotated corpus. However, the algorithm is designed in such a manner that it can easily be ported to other South Asian Languages provided the necessary NLP tools like POS tagger and chunker are available for that language. I compare results of Hindi data with English data of CONLL shared task of 2003. © 2008 Asian Federation of Natural Language Processing.

关键词： learning algorithms

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3rd international conference on machine learning and data mining in pattern recognition, MLDM 2003

引用

3rd international conference on machine learning and data mining, MLDM 2003

ISBN: (纸本)3540405046

The proceedings contain 37 papers. The special focus in this conference is on Decision Trees, Clustering and Its Application. The topics include: Introspective learning to build case-based reasoning CBR knowledge containers;graph-based tools for data mining and machine learning;simplification methods for model trees with regression and splitting nodes;learning multi-label alternating decision trees from texts and data;a discretization method of continuous attributes with guaranteed resistance to noise;on the size of a classification tree;a comparative analysis of clustering algorithms applied to load profiling;similarity-based clustering of sequences using hidden markov models;a fast parallel optimization for training support vector machine;a ROC-based reject rule for support vector machines;remembering similitude terms in CBR;authoring cases from free-text maintenance data;classification boundary approximation by using combination of training steps for real-time image segmentation;simple mimetic classifiers;novel mixtures based on the dirichlet distribution;estimating a quality of decision function by empirical risk;efficient locally linear embeddings of imperfect manifolds;dissimilarity representation of images for relevance feedback in content-based image retrieval;a rule-based scheme for filtering examples from majority class in an imbalanced training set;coevolutionary feature learning for object recognition;generalization of pattern-growth methods for sequential pattern mining with gap constraints;discover motifs in multi-dimensional time-series using the principal component analysis and the MDL principle and optimizing financial portfolios from the perspective of mining temporal structures of stock returns.

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Multi-relational data mining for Tetratricopeptide Repeats (TPR)-Like Superfamily Members in Leishmania spp.: Acting-by-Connecting Proteins

引用

3rd IAPR international conference on pattern recognition in Bioinformatics

作者： Girao, Karen T. Oliveira, Fatima C. E. Farias, Kaio M. Maia, Italo M. C. Silva, Samara C. Gadelha, Carla R. F. Carneiro, Laura D. G. Pacheco, Ana C. L. Kamimura, Michel T. Diniz, Michely C. Silva, Maria C. Oliveira, Diana M. Univ Estadual Ceara UECE Nucleo Tarcisio Pimenta Pesquisa Genom & Bioinfor NUGEN Fac Vet BR-60740000 Fortaleza Ceara Brazil

ISBN: (纸本)9783540884347

The multi-relational data mining (MrdM) approach looks for patterns that involve multiple tables from a relational database made of complex/structured objects whose normalized representation does require multiple tables. We have applied MrdM methods (relational association rule discovery and probabilistic relational models) with hidden Markov models (HMMs) and Viterbi algorithm (VA) to mine tetratricopeptide repeat (TPR), pentatricopeptide (PPR) and half-a-TPR (HAT) in genomes of pathogenic protozoa Leishmania. TPR is a protein-protein interaction module and TPR-containing proteins (TPRPs) act as scaffolds for the assembly of different multiprotein complexes. Our aim is to build a great panel of the TPR-like superfamily of Leishmania. Distributed relational state representations for complex stochastic processes were applied to identification, clustering and classification of Leishmania genes and we were able to detect putative 104 TPRPs, 36 PPRPs and 08 HATPs, comprising the TPR-like superfamily. We have also compared currently available resources (Pfam, SMART, SUPERFAMILY and TPRpred) with our approach (MrdM/HMM/VA).

关键词： Multi-relational data raining hidden Markov models Viterbi algorithm tetratricopeptide repeat motif Leishmania proteins

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Contents

Contents

引用

international conference on Intelligent System and Knowledge Engineering, ISKE

The following topics are dealt with: AI and expert systems; artificial immune systems and bio-informatics; chaos theory; data mining; fuzzy set theory; genetic algorithm; information retrieval; intelligent control; intelligent decision making; intelligent information processing; intelligent recognition; intelligent robotics; machine learning; natural language & machine translation; neural networks; rought set theory; support vector machine; swarm intelligence; video and image processing.

关键词： Artificial intelligence Intelligent robots Intelligent networks learning systems Intelligent control Expert systems Artificial immune systems Chaos data mining Fuzzy set theory

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A Survey on Statistical pattern Feature Extraction

引用

4th international conference on Intelligent Computing

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Jin, Fengxiang Shi, Zhongzhi China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221008 Peoples R China Acad Sinica Key Lab Intelligent Informat Proc Inst Comop Technol Beijing 100080 Peoples R China Agr Univ Coll Plant Protection Tai An Shandong 271018 Peoples R China Shandong Univ Sci Technol Coll Geoinformation Sci Engn Qingdao 266510 Peoples R China

ISBN: (纸本)9783540859833

The goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. pattern feature extraction is one of the most challenging research fields and has attracted the attention from many scholars. This paper summarily introduces the basic principle of SPFE, and discusses the latest progress of SPFE from the aspects such as classical statistical theories and their modifications, kernel-based methods, wavelet analysis and its modifications, algorithms integration and so on. At last we discuss the development trend of SPFE.

关键词： Extraction

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Attribute Division Algorithm Based on Entropy

Attribute Division Algorithm Based on Entropy

引用

international conference on Innovative Computing, Information and Control (ICICIC)

作者： Liyan Dong Minghui Sun Xiangshi Ren College of Computer Science and Technology Jilin University Changchun China Kochi University of Technology Kochi Japan

In order to preprocess data for data mining algorithms, an attribute division algorithm based on entropy is given through analyzing the physical meaning of information entropy. The algorithm measures the relativity among the different attributes based on entropy qualitatively and quantitatively. The original attribute set is divided into several subsets which are conditional independence by k-means clustering algorithm. Experimental results show that this algorithm can be used for data preprocessing.

关键词： Classification algorithms Clustering algorithms Entropy data mining Arrays Ash machine learning

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Chinese organization name recognition based on co-training algorithm

Chinese organization name recognition based on co-training a...

引用

international conference on Intelligent System and Knowledge Engineering, ISKE

作者： Xiao Ke Shaozi Li Department of Cognitive Science Xiamen University Xiamen China

Organization name recognition is the most difficult part in named entity recognition, in order to reduce the use of tagged corpus and use a large amount of untagged corpus, we firstly present using semi-supervised machine learning algorithm co-training combining with conditional random fields model and support vector machines on Chinese organization name recognition. Based on the principles of compatible and uncorrelated, we construct different classifiers from different views of conditional random fields model, and also construct different classifiers from two models of conditional random fields model and support vector machines as two views. Then present a heuristic untagged samples selection algorithm. From the experimental results we can see that, under the same F-measure, co-training algorithm simply use about 30% of the tagged data compared to single statistical model; under the same tagged data, co-training algorithm has an F-measure increase about 10% than single statistical model.

关键词： Support vector machines Support vector machine classification Hidden Markov models Tagging Intelligent systems Knowledge engineering Character recognition learning systems machine learning Cognitive science

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Support vector machine learning from positive and unlabeled samples

Support vector machine learning from positive and unlabeled ...

引用

international conference on Intelligent System and Knowledge Engineering, ISKE

作者： Ai-bing Ji Qi-ming Niu Ming-hu Ha College of Medicine Hebei University Baoding Hebei China College of Mathematics and Computer Hebei University Hebei China

In many machine learning settings, labeled samples are difficult to collect while unlabeled samples are abundant. We investigate in this paper the design of support vector machine classification algorithms learning from positive and unlabeled samples only. We first find the minimum bounding sphere that enclosed all the positive samples, and then use this minimum bounding sphere to pick out the negative samples from the unlabeled samples, at last we train the support vector machine using the training set which consists of the given positive samples and the negative samples picked out from the unlabeled samples. Experiments indicate that support vector machine learning from positive and unlabeled samples achieves the desired high test precision and prediction accuracy.

关键词： Support vector machines machine learning Support vector machine classification Intelligent systems Knowledge engineering Algorithm design and analysis Classification algorithms data mining learning systems Medical diagnostic imaging

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learning Algorithms with Boosting for Vector Quantization

Learning Algorithms with Boosting for Vector Quantization

引用

3rd international Symposium on Communications, Control, and Signal Processing (ISCCSP 2008), vol.1

作者： Hiromi Miyajima Noritaka Shigei Michiharu Maeda Shuji Hosoda Department of Electrical Electronics Engineering Kagoshima University Japan Fukuoka Institute of Technology Japan

There have been proposed many learning algorithms for VQ based on the steepest descend method. However, any learning algorithm known as a superior one does not always work well. This paper proposes a new learning algorithm with boosting. Boosting is a general method which attempts to boost the accuracy of any given learning algorithm. The proposed method consists of three sub-learners. The first sub-learner is constructed by performing the conventional learning algorithm with data randomly selected from given data space. The second sub-learner is constructed by performing the conventional learning algorithm with data selected with higher probability from data incorrectly learned by the first sub-learner. The third sub-learner is constructed with data for which either the first or the second sub-learner is incorrectly learned. That is, the method attempts to construct different kinds of reference vectors by using different kinds of data sets constructed from the original data set The output for any input data is given as decision by averaging the outputs of three sub-learners. In order to show the effectiveness of the proposed algorithm, numerical simulations are performed.

关键词： Boosting Vector quantization Numerical simulation learning systems Optimization methods data compression data mining pattern recognition Entropy

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