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检索条件"任意字段=International Conference of Soft Computing and Pattern Recognition"

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A Cyber Infrastructure for Evaluating the Performance of Human Centered Fusion

A Cyber Infrastructure for Evaluating the Performance of Hum...

引用

12th international conference on Information Fusion

作者： Hall, David L. McNeese, Michael D. Hellar, David B. Panulla, Brian J. Shumaker, Wade Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA

ISBN: (纸本)9780982443804

Humans are becoming an increasing important component of information fusion systems including;(1) acting as observers or "soft sensors" to augment traditional sensor systems, (2) participating in data analysis by using semantic reasoning and pattern recognition to augment computer processing, and (3) collaborating in ad hoc teams for situational analyses. These roles are supported by emerging information technologies such as social networking tools, advanced visual displays and cognitive aids. This paper describes a cyber-infrastructure and associated research strategy developed at Penn State University, to support human in the loop experiments to quantitatively evaluate concepts and tools for human centered information fusion. Examples of on-going experiments are also presented.

关键词： human experiments test and evaluation soft sensors hybrid reasoning

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Offline Handwritten Signature Identification and Verification Using Contourlet Transform

Offline Handwritten Signature Identification and Verificatio...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Muhammad Reza Pourshahabi Mohamad Hoseyn Sigari Hamid Reza Pourreza Machine Vision Research Laboratory Ferdowsi University of Mashhad Mashhad Iran

In this paper, a new offline handwritten signature identification and verification system based on contourlet transform is proposed. Contourlet transform (CT) is used as feature extractor in proposed system. Signature image is enhanced by removing noise and then it is normalized by size. After preprocessing stage, by applying a special type of contourlet transform on signature image, related contourlet coefficients are computed and feature vector is created. Euclidean distance is used as classifier. One of the most important features of proposed system is its independency from signer's nationality. Experimental results show that proposed system has so reliable results for both Persian and English signatures.

关键词： Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications Laboratories Computer industry

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Correlation Research of Association Rules and Application in the Data about Coronary Heart Disease

Correlation Research of Association Rules and Application in...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Zheng-kui Lin Wei-guo Yi Ming-yu Lu Zhi Liu Hao Xu Information Science and Technology Dalian Maritime University Dalian China Traditional and Western Medicine National Cardiovascular Center China-Japan Friendship Hospital Beijing China

The mining association rule is an important research field in data mining. The mining association rule usually adopts this model: support, confidence, interestingness. But this model can't measure the correlative degree between the antecedent and the consequent of the rule by ration. So we proposed a new mining model of association rules: support, coincidence, interestingness and analyzed the meaning of coincidence by instance. At last, we used this model in the data about coronary heart disease and obtained a lot of meaningful rules.

关键词： Association rules Cardiac disease Data mining Itemsets pattern recognition Hospitals Cardiology Information science Transaction databases Mathematics

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Automatic Multilevel Thresholding Using Binary Particle Swarm Optimization for Image Segmentation

Automatic Multilevel Thresholding Using Binary Particle Swar...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Leila Djerou Nacer Khelil Houssem Eddine Dehimi Mohamed Batouche Department Computer Science Med Khider University Biskra Algeria Department Mathematics Med Khider University Biskra Algeria Department Computer Science CCIS King Saud University Riyadh Saudi Arabia

In this paper an automatic multilevel thresholding approach, based on binary particle swarm optimization, is proposed. The proposed approach automatically determines the "optimum" number of the thresholds and simultaneously searches the optimal thresholds, by optimizing a function which uses the gray level thresholds as parameters. The algorithm starts with large number initial thresholds, then, these thresholds are dynamically refined to improve the value of the objective function. The proposed method is validated by illustrative examples; comparison with the exhaustive search Otsu's and Kapur's methods shows its efficiency.

关键词： Particle swarm optimization Image segmentation Computer science Pixel Iterative algorithms Ant colony optimization Entropy Genetic algorithms pattern recognition Mathematics

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DTM - Extracting Data Records from Search Engine Results Page Using Tree Matching Algorithm

DTM - Extracting Data Records from Search Engine Results Pag...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Jer Lang Hong Eugene Siew Simon Egerton Monash University Selangor Darul Ehsan Malaysia

In this paper, we develop a non-visual automatic wrapper for extracting data records from search engine results page. The novel techniques for our wrapper are (1) filtering rules to detect and filter out irrelevant data records, (2) a tree matching algorithm using frequency measures to increase the speed of data extraction (3) an algorithm to calculate the number and size of the components of data records to detect the correct data region. Results show that our wrapper is as robust and in many cases outperforms the state of the art wrappers such as ViNT and DEPTA. This wrapper could have significant speed advantages when processing large volumes of web sites data, which could be helpful in meta search engine development.

关键词： Data mining Search engines Tree data structures Matched filters Filtering algorithms Frequency measurement Metasearch pattern recognition pattern matching Velocity measurement

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Methodological Approach for Machine based Expression and Gender Classification

Methodological Approach for Machine based Expression and Gen...

引用

IEEE international Advance computing conference

作者： Rao, G. Mallikarjuna Babu, G. R. Kumari, G. Vijaya Chaitanya, N. Krishna Gokaraju Rangaraju Inst Engn & Technol Hyderabad 78 Andhra Pradesh India Jawaharlal Nehru Technol Univ CSE Hyderabad Andhra Pradesh India Jawaharlal Nehru Technol Univ Dept Comp Sci Hyderabad Andhra Pradesh India GRIET M Tech CSE Hyderabad Andhra Pradesh India

ISBN: (纸本)9781424429271

Machine based Gender classification is one of the challenging problem to the computer science researchers. The effortless ability exhibited by a two year kid for the same needs immense computation power for the computing machines. Many people attempted this problem by using different psychological characteristics such as handwriting, speech recognition, query response etc. These computational intensive techniques are application based and classification efficiency is limited. Some of the face identification systems have used the pixel based information, Eigen faces and geometrical relations of the facial features. We present a neural network-based upright invariant frontal face detection system which can classify the Gender based on the facial information. In our approach we club the pixel based and geometric facial features to increase the reliability of classification process. The use of pi-sigma neural network and the Cyclic Shift Invariance Technique enhances the robustness of classification process.

关键词： Face detection pattern recognition CSIT Pi-sigma network Machine learning

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Protein Fold pattern recognition Using Bayesian Ensemble of RBF Neural Networks

Protein Fold Pattern Recognition Using Bayesian Ensemble of ...

引用

international conference of soft computing and pattern recognition (SOCPAR)

作者： Homa Baradaran Hashemi Azadeh Shakery Mahdi Pakdaman Naeini Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Protein fold pattern recognition has been one of the most challenging problems in biology during the last 40 years. Recently due to the vast improvement in machine learning and pattern recognition methods many computer scientists have applied these methods to solve this problem. However, protein folding problem is much more complicated than ordinary machine learning problems because of its natural complexity imposed by the high dimensionality of feature space and diversity of different protein fold classes. To deal with such a challenging problem, we use an ensemble classifier model by applying MLP and RBF Neural Networks and Bayesian ensemble method. Also we have used the Laplace estimation method in order to smooth confusion matrices of the base classifiers. Experimental results imply that RBF Neural Network holds better Correct Classification Rate (CCR) compared to other common classification methods such as MLP networks. Our experiments also show that the Bayesian fusion method can improve the correct classification rate of proteins up to 20% with the final CCR of 59% by reducing both bias and variance error of the RBF classifiers, on a benchmark dataset containing 27 SCOP folds.

关键词： Proteins pattern recognition Bayesian methods Neural networks Artificial neural networks Biology computing Machine learning Amino acids Computer networks Neurons

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Fuzzy Gaussian Process Classification Model

Fuzzy Gaussian Process Classification Model

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6th international conference on Image Analysis and recognition

作者： Ahmed, Eman El Gayar, Neamat Atiya, Amir F. El Azab, Iman A. Cairo Univ Fac Comp & Informat Giza 12613 Egypt Nile Univ Sch Commun & Informat Technol Ctr Informat Sci Giza Egypt Cairo Univ Fac Engn Giza 12211 Egypt

ISBN: (纸本)9783642026102

soft labels allow a pattern to belong to multiple classes with different;degrees. In many real world applications the association of a pattern to multiple classes is more realistic;to describe overlap and uncertainties in class belongingness. The objective of this work is to develop a fuzzy Gaussian process model for classification of soft labeled data. Gaussian process models have gained popularity in the recent years in classification and regression problems and are example of a flexible, probabilistic, non-parametric model with uncertainty predictions. Here we derive a fuzzy Gaussian model for a, two class problem and then explain how this can be extended to multiple classes. The derived model is tested on different fuzzified datasets to show that it can adopt to various classification problems. Results reveal that our model outperforms the fuzzy K-Nearest Neighbor (FKNN), applied on the fuzzified dataset, as well as the Gaussian process and the K-Nearest Neighbor models used with crisp labels.

关键词： Frizzy Classification Gaussian Process(es) soft labels

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Template Design Using Extremal Optimization with Multiple Search Operators

Template Design Using Extremal Optimization with Multiple Se...

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Raymond Chiong Thomas Weise Bee Theng Lau School of Computing & Design Swinburne University of Technology Kuching Malaysia Nature Inspired Computation and Application Laboratory University of Science and Technology Hefei China

The template design problem is a constrained optimization problem originated from the printing industry. It involves printing several variations of a design onto one or more stencil sheets, where the aims are to minimize the number of stencils as well as the overproduction of prints of a particular design. Over the years, exact solution methods have been used to solve the problem. These methods could be useful for small to moderate-sized problem instances. However, when the problem instances are huge, the search space may easily grow too large for the systematic approaches. To date, no meta-heuristic or soft computing techniques have been used for this problem. In this paper, we propose the use of Extremal Optimization (EO) with multiple search operators for solving the template design problem. Different combinations of the search operators are tested via extensive numerical experiments. The results show that EO is indeed a feasible approach for template design optimization. The hybridization of EO with a deterministic local search has proven to be particularly effective.

关键词： Design optimization Printing Integer linear programming Constraint optimization Testing Containers pattern recognition Computer applications Laboratories Computer industry

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Cat Swarm Optimization for Clustering

Cat Swarm Optimization for Clustering

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international conference of soft computing and pattern recognition (SOCPAR)

作者： Budi Santosa Mirsa Kencana Ningrum Department of Industrial Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

Cat swarm optimization (CSO) is one of the new heuristic optimization algorithm which based on swarm intelligence. Previous research shows that this algorithm has better performance compared to the other heuristic optimization algorithms: Particle swarm optimization (PSO) and weighted-PSO in the cases of function minimization. In this research a new CSO algorithm for clustering problem is proposed. The new CSO clustering algorithm was tested on four different datasets. The modification is made on the CSO formula to obtain better results. Then, the accuracy level of poposed algorith was compared to those of K-means and PSO clustering. The modification of CSO formula can improve the performance of CSO clustering. The comparison indicates that CSO clustering can be considered as a sufficiently accurate clustering method.

关键词： Particle swarm optimization Clustering algorithms Cats Ant colony optimization Industrial engineering Probability Heuristic algorithms Informatics Computational intelligence pattern recognition

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