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pattern recognition with Linearly Structured Labels Using Recursive Kernel Estimator

10th international conference on Artificial Intelligence and soft computing (ICAISC 2010)

作者： Krzyzak, Adam Rafajlowicz, Ewaryst Concordia Univ Dept Comp Sci & Software Engn Montreal PQ Canada Wroclaw Univ Technol Inst Comp Engn Control Robotics Wroclaw Poland

ISBN: (纸本)9783642132070

We consider pattern recognition problem when classes and their labels are linearly structured (or ordered). We propose the loss function based on the squared differences between the true and the predicted class labels. The optimal Bayes classifier is derived and then estimated by the recursive kernel estimator. Its consistency is established theoretically. Its RBF-like realization of the classifier is also proposed together with a recursive learning algorithm, which is well suited for on-line applications. The proposed approach was tested in real life example involving classification of moving vehicles.

关键词： pattern recognition

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UANN BASED pattern CLASSIFIER USING ROUGH SET APPROACH

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2010年第7期24卷 1091-1109页

作者： Kothari, Ashwin Keskary, Avinash VNIT Dept Elect Engn Nagpur 440022 Maharashtra India

Data mining currently is a vital and core process because of advances in the technology, and numerous other forms of data. As an essential and significant constituent of data mining, classification has also become vital and a sought-after-option for research. Over the years, as various soft computing tools have been proven to be more complimentary than competitive, many hybrid approaches have evolved for pattern classification. A major thrust has always been there to apply hybrid approaches involving artificial neural network as the main tool for pattern classification. Here, one such hybrid approach is presented with application of Rough set philosophy to unsupervised artificial neural network (UANN) based pattern classifier. This enhances the performance of the classifier and makes it more suitable for real time classification applications dealing with large and unlabeled data. The idea presented here discusses with possibility and application of various Rough set based approaches at different levels of implementation of such classifier. The concluding section presents the results achieved for data of two case studies related to face recognition and mobile handoff prediction.

关键词： Rough sets pattern classification unsupervised neural network Rough neuron face recognition handoff

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Cellular Learning Automata with External Input and Its Applications in pattern recognition

Cellular Learning Automata with External Input and Its Appli...

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5th international conference on soft computing, computing with Words and Perceptions in System Analysis, Decision and Control

作者： Ahangaran, Meysam Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424434299

Cellular learning automata (CLA) which has been introduced recently, is a combination of cellular automata (CA) and learning automata (LA). A CLA is a CA in which a LA is assigned to its every cell. The LA residing in each cell determines the state of the cell on basis of its action probability vector. Like CA, there is a local rule that CLA operates under it. In this paper we introduce a new model of CLA in which each cell gets an external input vector from the environment in addition to reinforcement signal, so this model can work in non-stationary environments. Then two applications of the new model on image segmentation and clustering are given, and the results show that the proposed algorithm outperforms the similar algorithms.

关键词： Cells

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Differential Evolution Based on Population Reduction with Minimum Distance

Differential Evolution Based on Population Reduction with Mi...

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2011 IEEE international conference on Intelligent computing and Intelligent Systems(ICIS 2011)

作者： Ming Yang School of Computer Science China University of Geosciences Wuhan,China,430074 Jing Guan Institute for pattern recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan,China,430074 Zhihua Cai Changhe Li

In Differential Evolution(DE),there are many adaptive DE algorithms proposed for parameter adaptation. However,they mainly focus on tuning the mutation factor F and the crossover probability *** adaptation of population size NP has not been widely studied in the literature of DE. Reducing population size without jeopardizing the performance of an algorithm could save computational resources and hence accelerate it's convergence *** is beneficial to algorithms for optimization problems which need expensive evaluations. In this paper,we propose an improved population reduction method for DE,called dynNPMinD-DE,by considering the difference between *** the reduction criterion is satisfied,dynNPMinD-DE selects the best individual and pairs of individuals with minimal-step difference vectors to form a new ***-DE is tested on a set of 13 scalable benchmark functions in the number of dimensions of D=30 and D=50,*** results show that dynNPMinD-DE outperforms the other peer DE algorithms in terms of both solution accuracy and convergence speed on most test functions.

关键词： Index Terms-Differential evolution population size adaptation population reduction difference vector

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HISI: A soft Framework for Building recognition

HISI: A Soft Framework for Building Recognition

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5th international conference on soft computing, computing with Words and Perceptions in System Analysis, Decision and Control

作者： Awad, Mariette El Mobacher, Ayman Amer Univ Beirut Beirut Lebanon

ISBN: (纸本)9781424434299

In an effort to develop an interaction mechanism based on visual sensing to trigger and improve delivery of context based services and information pertinent to location in a solicited and near real time manner, we present in this paper HISI, a soft recognition approach to the processing and identification of buildings. Using a coarse joint histogram technique, an image captured by a mobile user with a cell phone is pre-processed to reduce the search space to an adaptive list of potential buildings, after which a weighted fusion of different SIFT maps identifies the building in question. Experimental results showed HISI's effectiveness with respect to other published results in the literature and motivates further extensions to HISI, as it carries expectations for commercial values to the delivery of context based services initiated by users.

关键词： Location based services

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Research on pattern recognition Based on the Universal Electronic Tongue System

Research on Pattern Recognition Based on the Universal Elect...

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6th international conference on Wireless Communications, Networking and Mobile computing (WICOM)

作者： Zhou, Jianhui Lei, Wang Teng, Jionghua Tao, Zhou Tongji Univ Coll Elect & Informat Engn Shanghai 200092 Peoples R China Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China

ISBN: (纸本)9781424437092

Chemical taste is indispensable information in food testing. The technical of electronic tongue system is one of research directions to identify different chemical tastes. This paper focuses on the pattern recognition method based on learning vector quantization (LVQ) neural network. The electronic tongue system designed could identify all the samples of beer, fruit juice and milk successfully in the experiments. The result shows that LVQ neural network is applicable in the pattern recognition of electronic tongue system and can also be used on condition that information is gathered by multisensors array. The pattern recognition methods of the universal electronic tongue are proposed in this paper. The effective universal electronic tongue has much advantage over others such as simple methods of pattern recognition and classification, easy training approaches and wider application fields.

关键词： LVQ neural network pattern recognition universal electronic tongue system taste recognition

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pattern classification with missing data: a review

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NEURAL computing & APPLICATIONS 2010年第2期19卷 263-282页

作者： Garcia-Laencina, Pedro J. Sancho-Gomez, Jose-Luis Figueiras-Vidal, Anibal R. Univ Politecn Cartagena Dpto Tecnol Informac & Comunicac Murcia 30202 Spain Univ Carlos III Madrid Dpto Teoria Senal & Comunicac Madrid 28911 Spain

pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

关键词： pattern classification Missing data Neural networks Machine learning

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pattern Classification Using Class-Dependent Rough-Fuzzy Granular Space 1

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5th international conference on Rough Set and Knowledge Technology (RSKT)

作者： Pal, Sankar K. Meher, Saroj K. Dutta, Soumitra Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India INSEAD F-77305 Fontainebleau France

ISBN: (数字)9783642162480

ISBN: (纸本)9783642162473

The article describes a new rough-fuzzy model for pattern classification. Here, class-dependent granules are formulated in fuzzy environment that preserve better class discriminatory information. Neighborhood rough sets (NRS) are used in the selection of a subset of granulated features that explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of class-dependent fuzzy granulation and NRS that is useful in pattern classification with overlapping classes. The superiority of the proposed model to other similar methods is demonstrated with both completely and partially labeled data sets using various performance measures. The proposed model learns well even with a lower percentage of training set that makes the system fast.

关键词： Fuzzy information granulation rough neighborhood sets rough-fuzzy granular computing pattern recognition soft computing

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Towards Application of soft computing in Structural Health Monitoring

Towards Application of Soft Computing in Structural Health M...

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10th international conference on Artificial Intelligence and soft computing (ICAISC 2010)

作者： Nazarko, Piotr Ziemianski, Leonard Rzeszow Univ Technol Dept Struct Mech PL-35959 Rzeszow Poland

ISBN: (纸本)9783642132315

The paper presents preliminary results of data analysis and discusses the application of soft computing methods in the field of non-destructive tests. The main objective of developed diagnostic system are the automatic detection and evaluation of damage. Thus the system is composed of two signal processing techniques known as novelty detection and pattern recognition. For this purpose autoassociative as well as feed-forward neural networks are used. All the signals used for training the system are obtained from laboratory tests of strip specimens, where phenomenon of elastic wave propagation in solids was utilized. Computed parameters of time signals defines various types of input vectors used for training neural networks. The results finally obtained prove that the proposed diagnostic system made automation of structure testing possible and can be applied to Structural Health Monitoring.

关键词： Neural networks novelty detection damage evaluation elastic waves signal processing structural health monitoring

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A novel method for splice sites recognition using comprehensive information

A novel method for splice sites recognition using comprehens...

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international conference on Pervasive computing, Signal Processing and Applications

作者： Wang, Kejun Lv, Junjie Feng, Weixing Wang, Xin College of Automation Harbin Engineering University Harbin 150001 China

ISBN: (纸本)9780769541808

To identify splice sites more accurately and efficiently, a method for the recognition of splice sites based on comprehensive information is proposed. By analyzing the splicing signals, splicing sequences, secondary structures of flank sequence, different splicing factor mechanism of action and other characteristics of donor sites and acceptor sites, donor sites identification signal model, acceptor sites identification signal model, donor sites identification sequence model, acceptor sites identification sequence model were built respectively. Then the Mfold package in Vienna soft was used to predict the most stable secondary structure of flank sequences. The traditional four-letter alphabet was converted into eight-letter alphabet sequence. The sequence-structure combination strings were used for training signal models, sequence models, then recognized splice sites by the well trained models. Our results show that the accuracy of splice site recognition is greater than 95%, suggesting that the method has great potential to achieve a good performance for splice sites identification. © 2010 IEEE.

关键词： Mfold package Vienna soft alternative splice biology computing comprehensive information flank sequences macromolecules molecular biophysics pattern recognition secondary structure sequence-structure combination strings sequences splice sites splice sites recognition splicing sequences splicing signals

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