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Mutual Information based recognition of Pulse Signal in TCM

Mutual Information based Recognition of Pulse Signal in TCM

the 4th international conference on Bioinformatics and Biomedical Engineering(第四届IEEE生物信息与生物医学工程国际会议 iCBBE 2010)

作者： Yong Shen Chiheng Zhou Chunming Xia Jianjun Yan Rui Guo Haixia Yan Fufeng Li Yiqin Wang Center for Mechatronics Engineering East China University of Science and Technology Shanghai 200237 Center for TCM information science and technology Shanghai University of TCM Shanghai 201203 P.R.Ch

this study aims at utilizing mutual information algorithm to make an objective and quantitative research for the pulses in Traditional Chinese Medicine (TCM) diagnosis. the normal pulse signals, slippery pulse signals and taut pulse signals were collected from the outpatients by Shanghai University of TCM. Some typical pulse signals from all these classes were selected as the templates to be matched with the new pulse signals by means of mutual information, and then which class the new pulse signal should be classified was determined through the maximum mutual information. Finally, the satisfactory results were obtained which proof that it was appropriate to use mutual information to recognize pulse signals. the biggest advantage of this way is simple, since the inputted pulse signals should not be extracted features. And the time-domain features were extracted from the pulse signals to be classified. the results from the two different ways show the classification method presented is efficient based on Mutual Information.

关键词： Mutual information Feature extraction Random variables data mining Support vector machines Support vector machine classification Neural networks Medical diagnostic imaging Time domain analysis pattern recognition

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A Svm-Based Algorithm for Automatic Species Classification of a Marine Diatom Genus Coscinodiscus Ehrenberg

A Svm-Based Algorithm for Automatic Species Classification o...

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international conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Jinfei Luo Qiaoqi Luo Yahui Gao Changping Chen Junrong Liang Chenhui Yang Sch. of Life Sci. Xiamen Univ. Xiamen China Sch. of Inf. Sci. & Technol. Xiamen Univ. Xiamen China

Coscinodiscus Ehrenberg is a large and ecologically important diatom genus with plentiful species in marine phytoplankton and with a variety of round shapes and ornamentation. these properties can be measured by computer image pre-segmentation and feature extraction with threshold methods. However, it proves to be complicated task because of the high spatial variability of ornamentation properties. Researchers have shown a Teach-program and a number of library functions operating on sample image lists (SIL's) and operating on classifiers (CF's) to solve the problem. In this paper, we present Coscinodiscus Ehrenberg ornamentation classifier algorithm called support vector machines (SVMs) to derive a new set of SIL's and CF's. the principal purpose of SVMs is Coscinodiscus Ehrenberg images pattern recognition approach. A pattern is in this context always the SIL's contained in a sub-rectangle of some given (possibly larger) image. For the same classifier this sub-rectangle must always have the same dimensions, while the query image to be searched may be arbitrarily large. the training is done by preparing SIL's for the pattern taxa in question and feeding them to CF's created. Our classifier generation with preprocessing code optimization achieves a AAAAA preprocessing code, a 0.981 learning success, a 100% computational complexity. Train with SIL's achieves 212 samples, 17 taxa, a 0.472% error rate and Test with Query image searching achieves 253 samples, 17 taxa, a 15.81% error rate. the experiments demonstrate that the proposed method is very robust to the threshold segmentation and ornamentation feature extraction of Coscinodiscus Ehrenberg images, and is effective and useful for species classification of Coscinodiscus Ehrenberg.

关键词： Feature extraction Error analysis Shape Libraries Support vector machines Support vector machine classification pattern recognition Computational complexity Testing Robustness

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MICAI 2010 Organization and conference Committee

Proceedings of Special Session - 9th Mexican International C...

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Proceedings of Special Session - 9th Mexican international conference on Artificial Intelligence: Advances in Artificial Intelligence and Applications, MICAI 2010 2010年 viii页

作者： Reyes-García, Carlos Alberto Sidorov, Grigori Hernández-Aguirre, Arturo Arroyo, Gustavo Murrieta, Rafael Gonzalez, Jesus A. Gonzalez, Miguel Herrera, Oscar Peña, Alejandro Espinoza, Félix A. Castro Cansino, Joel Suárez Galicia-Haro, Sofia N. Koeppen, Mario Reyes-García, Carlos A. Monroy, Raul Gelbukh, Alexander Mezura-Montes, Efrén Leguizamón, Guillermo Ramírez-Manzanares, Alonso Castillo, Oscar Fuentes, Olac Sánchez, Gildardo Natural Language Processing Mexico Machine Learning and Pattern Recognition Mexico Hybrid Intelligent Systems and Neural Networks Mexico Logic Reasoning Ontologies Knowledge Mgmt. Knowledge-Based Syst. Multi-agent Syst. Mexico Data Mining Mexico Intelligent Tutoring Systems Mexico Evolutionary Algorithms and Other Naturally Inspired Algorithms Mexico Computer Vision and Image Processing Mexico Fuzzy Logic Uncertainty and Probabilistic Reasoning Mexico Bioinformatics and Medical Applications Mexico Robotics Planning and Scheduling Mexico

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thalamic Bursts Mediate pattern recognition

Thalamic Bursts Mediate Pattern Recognition

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4th international IEEE/EMBS conference on Neural Engineering

作者： Jandel, Magnus Swedish Def Res Agcy Stockholm Sweden

ISBN: (纸本)9781424420728

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： thalamus thalamic reticular nucleus neocortex pattern recognition kernel machine support vector

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CLASSIFICATION OF FACIAL EXPRESSIONS USING data mining AND machine learning ALGORIthMS 4

CLASSIFICATION OF FACIAL EXPRESSIONS USING DATA MINING AND M...

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4th Iberian conference on Information Systems and Technologies

作者： Faria, Brigida Monica Lau, Nuno Reis, Luis Paulo Univ Aveiro Dept Elect Telecomunicacoes & Informat Aveiro Portugal IPP ESTSP Oporto Portugal IEETA Aveiro Portugal Univ Porto Fac Engn DEI Oporto Portugal Univ Porto LIACC Oporto Portugal

ISBN: (纸本)9789899624702

machine Facial recognition is a problem that consists in exploring still or video images of a scene to identify or verify one or more persons in the scene using stored databases of faces. Research in Facial recognition is multidisciplinary including disciplines like pattern recognition, image processing and analysis, computer graphics, machine learning and data mining. Sometimes applications include not only facial recognition but also facial expression characteristics identification and classification like blinking of the eyes, open/close mouth among others. this paper presents a comparative study of three algorithms for classification basic facial expressions.

关键词： data mining OneR Naive Bayes Support Vector machines Facial recognition

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Optimal Double-Kernel Combination for Classification

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

作者： Wang, Feng Zhang, Hongbin Beijing Univ Sci & Technol Coll Comp Sci Beijing 100124 Peoples R China

ISBN: (纸本)9783642030697

Traditional kernelised classification methods Could not perforin well sometimes because of the using of a single and fixed kernel, especially oil sonic complicated data sets. In this paper. a novel optimal double-kernel combination (ODKC) method is proposed for complicated classification tasks. Firstly, data sets are mapped by two basic kernels into different feature spaces respectively, and then three kinds of optimal composite kernels are constructed by integrating information of the two feature spaces. Comparative experiments demonstrate the effectiveness of our methods.

关键词： Classification kernel learning pattern recognition

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An Evidence-Driven Probabilistic Inference Framework for Semantic Image Understanding

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

作者： Nikolopoulos, Spiros Papadopoulos, Georgios th. Kompatsiaris, Ioannis Patras, Ioannis CERTH Informat & Telemat Inst 6th Km Charilaou Thermi Rd Thessaloniki Greece Univ London Queen Mary Sch Elect Engn & Comp Sci London E1 4NS England

ISBN: (纸本)9783642030697

this work presents an image analysis framework driven by emerging evidence and constrained by the semantics expressed in an ontology. Human perception, apart from visual stimulus and pattern recognition, relies also on general knowledge and application context for understanding visual content in conceptual terms. Our work is an attempt to imitate this behavior by devising an evidence driven probabilistic, inference framework using ontologies and bayesian networks. Experiments conducted for two different image analysis, tasks showed improvement performance, compared to the case where computer vision techniques act isolated from any type of knowledge or context.

关键词： Image analysis

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On Fixed Convex Combinations of No-Regret Learners

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

作者： Calliess, Jan-P. Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

ISBN: (纸本)9783642030697

No-regret algorithms for online convex optimization are potent online learning tools and have been demonstrated to be successful in a wide-ranging number of applications. Considering affine and external regret, we, investigate what happens when a set of no-regret learners (voters) merge their respective decisions in each learning iteration to a single, common one in form of a convex combination. We show that an agent (or algorithm) that executes this merged decision in each iteration of the online learning process and each time feeds back a copy of its own reward function to the voters, incurs sublinear regret itself. As a by-product, we obtain a simple method that allows us to construct new no-regret algorithms out of known ones.

关键词： learning algorithms

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the Effect of Domain Knowledge on Rule Extraction from Support Vector machines

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

作者： Barakat, Nahla Bradley, Andrew P. German Univ Technol Oman Muscat Oman Univ Queensland Sch Informat Technol & Engn ITEE St Lucia Qld 4072 Australia

ISBN: (纸本)9783642030697

Prior knowledge about it problem domain can be utilized to bias Support Vector machines (SVMs) towards learning better hypothesis functions. To this end, a number of methods have been proposed that demonstrate improved generalization performance after the application of domain knowledge;especially in the case of scarce training data. In this paper, we propose an extension to the Virtual Support vectors (VSVs) technique where only a subset of the Support vectors (SVs) is Utilized. Unlike previous methods, the Purpose here is to compensate for noise and uncertainty in the training data. Furthermore, we investigate the effect of domain knowledge not only oil the quality of the SVM model, but also Oil rules extracted from it: hence the learned pattern by the SVM. Results on five benchmark and one real life data sets show that domain knowledge can significantly improve both the quality Of the SVM and the rules extracted from it.

关键词： data mining machine learning Domain knowledge utilization Rule learning Support Vector machines

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A New Hybrid Approach for data Clustering

A New Hybrid Approach for Data Clustering

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international Symposium on Telecommunications

作者： Danial Yazdani Sara Golyari Mohammad Reza Meybodi Islamic azad university Department of Computer Engineering and Information Technology Amirkabir University of Technology

ISBN: (纸本)9781424481835

data clustering has been applied in multiple fields such as machine learning, data mining, wireless sensor networks and pattern recognition. One of the most famous clustering approaches is K-means which effectively has been used in many clustering problems, but this algorithm has some problems such as local optimal convergence and initial point sensitivity. Artificial fishes swarm algorithm (AFSA) is one of the swarm intelligent algorithms and its major application is in solving optimization problems. Of its characteristics, it can refer to high convergent rate and insensitivity to initial values. In this paper a hybrid clustering method based on artificial fishes swarm algorithm and K-means so called KAFSA is proposed. In the proposed algorithm, K-means algorithm is used as one of the behaviors of artificial fishes in AFSA. the proposed algorithm has been tested on five data sets and its efficiency was compared with particle swarm optimization (PSO), K-means and standard AFSA algorithms. Experimental results showed that proposed approach has suitable and acceptable efficacy in data clustering.

关键词： Artificial fishes swarm algorithm data clustering Optimization K-means PSO

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