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IEEE international Workshop on Anti-counterfeiting, Security, Identification, ASID

作者： Jing Shao Xinyu Chen Ping Guo Beijing Normal University Beijing China

In this paper, we propose a method by engaging the one class support vector machine (OC-SVM) in the identification of diffractive optically variable images (DOVIs). OC-SVM, as a special SVM, can solve the problems of high-dimensional data sets and small sample size (SSS) with positive and negative unbalance training data. Image feature matrix is built by extracting image features from texture aspects. OC-SVM can be trained with the high-dimensional matrix directly, and does not have to reduce the dimensionality of feature matrix as the usual methods. The experiment results show the effectiveness of the proposed approach against linear discriminant analysis. Considering time cost and correct classification rate, OC-SVM is suitable for the identification of DOVIs.

关键词： Support vector machines Optical diffraction Holography Holographic optical components Linear discriminant analysis Support vector machine classification pattern recognition Feature extraction Eyes Training data

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Theme detection an exploration of opinion subjectivity

Theme detection an exploration of opinion subjectivity

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international conference on Affective Computing and Intelligent Interaction and Workshops, ACII

作者： Amitava Das Sivaji Bandyopadhyay Department of Computer Science and Engineering Jadavpur University Kolkata India

Work in opinion mining and classification often assumes the incoming documents to be opinionated. Opinion mining system makes false hits while attempting to compute polarity values for non-subjective or factual sentences or documents. It becomes imperative to decide whether a given document contains subjective information or not as well as to identify which portions of the document are subjective or factual. In this work a theme detection technique has been evolved for more generic domain independent subjectivity detection that classifies sentences with binary feature: opinionated or non-opinionated. Theme detection technique examines sentence level opinion and finally accumulates the opinion clues to reach the discourse level subjectivity. The subjectivity detection system has been evaluated on the multi perspective question answering (MPQA) corpus as well as on Bengali corpus. The system evaluation has shown the precision and recall values of 76.08 and 83.33 for English and 72.16 and 76.00 for Bengali respectively.

关键词： Computer science Speech data mining machine learning Text analysis Natural languages System testing Tagging Labeling Clustering algorithms

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Difficult detection: A comparison of two different approaches to eye detection for unconstrained environments

Difficult detection: A comparison of two different approache...

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IEEE international conference on Biometrics: Theory, Applications, and Systems (BTAS)

作者： Walter J. Scheirer Anderson Rocha Brian Heflin Terrance E. Boult University of Colorado Colorado Springs Colorado Springs CO USA Securics Inc. Colorado Springs CO USA Institute of Computing State University of Campinas-UNICAMP Sao Paulo Brazil

Eye detection is a well studied problem for the constrained face recognition problem, where we find controlled distances, lighting, and limited pose variation. A far more difficult scenario for eye detection is the unconstrained face recognition problem, where we do not have any control over the environment or the subject. In this paper, we take a look at two different approaches for eye detection under difficult acquisition circumstances, including low-light, distance, pose variation, and blur. A new machine learning approach and several correlation filter approaches, including a new adaptive variant, are compared. We present experimental results on a variety of controlled data sets (derived from FERET and CMU PIE) that have been re-imaged under the difficult conditions of interest with an EMCCD based acquisition system. The results of our experiments show that our new detection approaches are extremely accurate under all tested conditions, and significantly improve detection accuracy compared to a leading commercial detector. This unique evaluation brings us one step closer to a better solution for the unconstrained face recognition problem.

关键词： Face detection Face recognition Optical sensors Computer vision Gas detectors Springs machine learning Biosensors Lighting control Adaptive filters

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Feature selection and construction for the discrimination of neurodegenerative diseases based on gait analysis

Feature selection and construction for the discrimination of...

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international conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth

作者： Mingjing Yang Huiru Zheng Haiying Wang Sally McClean Faculty of Computing and Engineering University of Ulster UK

Gait disorder is one symptom of neurodegenerative disease. Using wearable motion sensors to monitor the motor function of patients with neurodegenerative disease has attracted more attention. Research has shown that machine learning techniques can be applied to the classification of neurodegenerative diseases from the gait data recorded by footswitches. In order to identify the most valuable features from 10 raw temporal variables extracted from gait cycles to improve the classification performance, we examine four types of feature selection and construction methods, namely, maximum signal-to-noise ratio based feature selection method, maximum signal-to-noise ratio combined with minimum correlation based feature selection method, maximum prediction power combined with minimum correlation based feature selection method and principal component analysis. Results show that using a set of four features, a relatively high prediction performance has been achieved with classification accuracies ranging from 79.04% to 93.96%. The continual increase of the number of features does not significantly contribute to the improvement of classification performance. This is consistent with clustering-based feature analysis.

关键词： Parkinson's disease Biomedical monitoring Wearable sensors Patient monitoring machine learning Backpropagation Legged locomotion data mining Signal processing Support vector machines

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Enzyme Function Classification using Protein Sequence Features and Random Forest

Enzyme Function Classification using Protein Sequence Featur...

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The 3rd international conference on Bioinformatics and Biomedical Engineering(iCBBE 2009)(第三届生物信息与生物医学工程国际会议)

作者： Chetan Kumar Gang Li Alok Choudhary Department of Electrical Engineering and Computer Science Northwestern University EvanstonILUSA

Enzymes are proteins that catalyze bio-chemical reactions in different ways and play important roles in metabolic pathways. The exponential rise in sequences of new enzymes has necessitated developing methods that accurately predict their function. To address this problem, approaches that cluster enzymes based on their sequence and structural similarity have been applied, but are known to fail for dissimilar proteins that perform the same function. In this paper, we present a machine learning approach to accurately predict the main function class of enzymes based on a unique set of 73 sequence-derived features. Our features can be extracted using freely available online tools. We used different multi-class classifiers to categorize enzyme protein sequences into one of the NC-IUBMB defined six main function classes. Amongst the classifiers, Random Forest reported the best results with an overall accuracy of 88％ and precision and recall in the range of 84％ to 93％ and 82％ to 93％ respectively. Our results compare favorably with existing methods, and in some cases report better performance. Random Forest has been proven to be a very efficient data mining algorithm. This paper is first in exploring their application to enzyme function prediction. The datasets can be accessed online at the location: .

关键词： enzyme function classificaiton machine learning multi-class random forest protein sequence features

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learning Classifiers without Negative Examples: A Reduction Approach

Learning Classifiers without Negative Examples: A Reduction ...

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3rd international conference on Digital Information Management

作者： Zhang, Dell Lee, Wee Sun Univ London SCSIS Birkbeck London WC1E 7HX England Natl Univ Singapore Dept Comp Sci Singapore 117548 Singapore

ISBN: (纸本)9781424429165

The problem of PU learning, i.e., learning classifiers with positive and unlabelled examples (but not negative examples), is very important in information retrieval and data mining. We address this problem through a novel approach: reducing it to the problem of learning classifiers for some meaningful multivariate performance measures. In particular, we show how a powerful machine learning algorithm, Support Vector machine, can be adapted to solve this problem. The effectiveness and efficiency of the proposed approach have been confirmed by our experiments on three real-world datasets.

关键词： data mining

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A Neuro-Genetic Framework for pattern recognition in Complex Systems

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FUNDAMENTA INFORMATICAE 2008年第2期87卷 207-226页

作者： Bandini, Stefania Vanneschi, Leonardo Wuensche, Andrew Shehata, Alessandro Bahgat Univ Milano Bicocca Complex Syst & Artificial Intelligence CSAI Res C Dept Informat Syst & Commun DISCo I-20126 Milan Italy Univ Sussex Dept Informat Sch Sci & Technol Brighton BN1 9RH E Sussex England

This paper presents a general framework to automatically generate rules that produce given spatial patterns in complex systems. The proposed framework integrates Genetic Algorithms with Artificial Neural Networks or Support Vector machines. Here, it is tested on a well known 3-values, 6-neighbors, k-totalistic cellular automata rule called the "burning paper" rule. Results are encouraging and should pave the way for the use of the proposed framework in real-life complex systems models.

关键词： Complex Systems Cellular Automata pattern recognition Automatic Rule Generation machine learning Genetic Algorithms Artificial Neural Networks Support Vector machines

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An engineering toolbox to build situation aware ambient assisted living systems

An engineering toolbox to build situation aware ambient assi...

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3rd international conference on Broadband Communications, Informatics and Biomedical Applications, BroadCom 2008

作者： Kurschl, Werner Mitsch, Stefan Schoenboeck, Johannes Upper Austria University of Applied Sciences Research Center Hagenberg Department of Software Engineering Softwarepark 11 A-4232 Hagenberg Austria

ISBN: (纸本)9780769534534

Due to increasing anticipated average life and health expenditure ambient assisted living (AAL) systems attract the attention of researchers. To successfully build and deploy AAL systems knowledge from different fields of computer science is needed: pervasive computing to gain the raw data, machine learning and pattern recognition to interpret these data and HCI knowledge to allow implicit interaction with the system. In this paper we propose a reference architecture for building AAL systems. Based on this reference architecture we introduce a toolbox that simplifies the development of AAL systems. The toolbox consists of a meta-model for pipeline systems, a low-level context model, high-level context ontologies, customizable components and tool support. © 2008 IEEE.

关键词： pattern recognition

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Cascade Boosting LBP Feature Based Classifiers for Face recognition

Cascade Boosting LBP Feature Based Classifiers for Face Reco...

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3rd international conference on Intelligent System and Knowledge Engineering

作者： Ma, Canming Tan, Taizhe Yang, Qunsheng Guangdong Univ Technol Fac Comp Guangzhou 510006 Guangdong Peoples R China

ISBN: (纸本)9781424421961

Local Binary pattern (LBP) is a powerful means of texture description that has achieved great success in face analysis area. In this paper, we propose a face recognition approach using boosted LBP-feature based classifiers. The multi-class problem of face recognition is transformed into a two-class one of intra- and extra-class by classifying every pair of face image as intra-class or extra-class ones. The cascade framework, is used to overcome the problem of overwhelmingly large number of samples and grossly imbalance of the positive and negative samples. By boot-strapping negative examples, sub-training spaces (random subsets) are randomly generated, and then weak classifiers are learned using every sub-training space (random subset). The weak classifiers are combined into a strong one by improving recognition accuracy. Experimental results on FERET database show competitive performance.

关键词： Boosting Face recognition Bayesian methods machine learning algorithms Intelligent systems Knowledge engineering Image recognition Principal component analysis Linear discriminant analysis Pixel

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pattern recognition Based on Weighted and Supervised ART2

Pattern Recognition Based on Weighted and Supervised ART2

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3rd international conference on Intelligent System and Knowledge Engineering

作者： Chu Na Ma Lizhuang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200030 Peoples R China

ISBN: (纸本)9781424421961

It is crucial for TCM (Traditional Chinese Medicine) post-hepatitis cirrhosis diagnosis to accurately identify the syndrome. Meanwhile, the selection of features which are relevant to a certain TCM post-hepatitis cirrhosis syndrome not only improves the performance of the classifiers, but also provides well measure for treatment. Therefore, in this paper, we analyze the classical ART2(Adaptive Resonance Theory 2) neural network such as the problem of pattern drifting and the same phase data with different amplitudes. Based on this, here, a novel network named SWART2 is proposed by taking dispersion testing and centroid computation learning, and introducing the weighted and supervised mechanism, which aims at improving ART2's ability of classification greatly for post-hepatitis cirrhosis diagnosis. Experimental results in this paper showed that the ***2 performed better than classical ART2.

关键词： pattern recognition Knowledge engineering Neural networks pattern matching Intelligent systems Resonance Testing Artificial neural networks Computer science Medical diagnostic imaging

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