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检索条件"任意字段=International Conference on Soft Computing and Pattern Recognition, SoCPaR 2009"

共 257 条记录，以下是131-140 订阅

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Integrated Feature Selection and Parameter Optimization for Evolving Spiking Neural Networks using Quantum Inspired Particle Swarm Optimization

Integrated Feature Selection and Parameter Optimization for ...

引用

international conference of soft computing and pattern recognition

作者： Hamed, Haza Nuzly Abdull Kasabov, Nikola Shamsuddin, Siti Mariyam Auckland Univ Technol KEDRI Auckland New Zealand Univ Teknol Malaysia Soft Computing Res Grp Johor Baharu Johor Malaysia

ISBN: (纸本)9781424453306

This paper proposes a novel method for optimizing features and parameters in the Evolving Spiking Neural Network (ESNN) using Quantum-inspired Particle Swarm Optimization (QiPSO). This study reveals the interesting concept of QiPSO in which information is represented as binary structures. The mechanism simultaneously optimizes the ESNN parameters and relevant features using wrapper approach. A synthetic dataset is used to evaluate the performance of the proposed method. The results show that QiPSO yields promising outcomes in obtaining the best combination of ESNN parameters as well as in identifying the most relevant features.

关键词： Evolving Spiking Neural Network Particle Swarm Quantum computing Feature Optimization Parameter Optimization

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Review of Artificial Immune System in Web Personalization

Review of Artificial Immune System in Web Personalization

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international conference of soft computing and pattern recognition

作者： Rastegari, Hamid Shamsuddin, Siti Mariyam Univ Technol Malaysia Fac Comp Sci & Informat Syst Skudai Malaysia

ISBN: (纸本)9781424453306

This paper is a review on knowledge discovery in the field of web mining for the benefit of research on the personalization of web-based information services. The essence of personalization is the adaptability of information systems to the needs of their users. This issue is becoming increasingly important on the Web, as non-expert users are overcame by the quantity of information available online. This article investigates the application of artificial immune systems (AIS) to knowledge discovery as a web personalization tool. AIS are thought to confer the adaptability and learning required for this task.

关键词： Web Personalization Artificial Immune System Web Usage Mining pattern Discovery Clonal Selection

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socpar 2009 - soft computing and pattern recognition

SoCPaR 2009 - Soft Computing and Pattern Recognition

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international conference on soft computing and pattern recognition, socpar 2009

ISBN: (纸本)9780769538792

The proceedings contain 137 papers. The topics discussed include: a new clustering method based on weighted kernel K-means for non-linear data;a review of recent alignment-free clustering algorithms in expressed sequence tag;league championship algorithm: a new algorithm for numerical function optimization;an improved discrete particle swarm optimization in evacuation planning;PSO-based optimization of state feedback tracking controller for a flexible link manipulator;a novel fuzzy histogram based estimation of distribution algorithm for global numerical optimization;rule modeling engine for optimizing complex event processing patterns;correlation research of association rules and application in the data about coronary heart disease;linear antenna array synthesis with invasive weed optimization algorithm;robust type-2 fuzzy control of an automatic guided vehicle for wall-following;and a modified differential evolution algorithm and its application to engineering problems.

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Synergistic-ANN Recognizers for Monitoring and Diagnosis of Multivariate Process Shift patterns

Synergistic-ANN Recognizers for Monitoring and Diagnosis of ...

引用

international conference of soft computing and pattern recognition

作者： Masood, Ibrahim Hassan, Adnan Univ Tun Hussein Onn Malaysia Fac Mech & Mfg Engn Batu Pahat Malaysia Univ Teknol Malaysia Fac Mech Engn Skudai Malaysia

ISBN: (纸本)9781424453306

An intelligent control chart pattern recognition system is essential for efficient monitoring and diagnosis process variation in automated manufacturing environment. Artificial neural networks (ANN) have been applied for automated recognition of control chart patterns since the last 20 years. In early study, the development of control chart patterns recognizers was mainly based on generalized-ANN model. There has been an increasing trend among researchers to move beyond generalized recognizer particularly for addressing complex recognition tasks. However, the existing works mainly focus on univariate process cases. This paper aims to investigate an effective synergistic-ANN model for on-line monitoring and diagnosis multivariate process patterns. The recognition performances of a generalized-ANN and the parallel distributed ANN recognizers for learning dynamic patterns of multivariate process patterns were discussed.

关键词： ANN control chart patterns control chart pattern recognition generalized-ANN synergistic-ANN statistical process control

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Protein Fold pattern recognition Using Bayesian Ensemble of RBF Neural Networks

Protein Fold Pattern Recognition Using Bayesian Ensemble of ...

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international conference of soft computing and pattern recognition

作者： Hashemi, Homa Baradaran Shakery, Azadeh Naeini, Mahdi Pakdaman Univ Tehran Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781424453306

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： Protein Folding Bayesian Classifier Fusion RBF MLP

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3D Volumetric CT Liver Segmentation Using Hybrid Segmentation Techniques

3D Volumetric CT Liver Segmentation Using Hybrid Segmentatio...

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international conference of soft computing and pattern recognition

作者： Yussof, Wan Nural Jawahir Wan Burkhardt, Hans Univ Freiburg Chair Pattern Recognit & Image Proc Freiburg I Br Germany

ISBN: (纸本)9781424453306

The first step for computer-aided diagnosis for liver of CT scans is the identification of liver region. To deal with multislice CT scans, automatic liver segmentation is required. In this paper, we propose a liver segmentation algorithm using hybrid techniques by combining morphological-based, region-based and histogram-based techniques to segment volumetric CT data. A morphological-based technique is used to find the initial liver tissue from the first slice which is set as a starting slice and region-based is used for further processing for the rest slices, which incorporates seed point generation from Euclidean distance transform (EDT) image on the previous slice for region growing on the current slice. In order to remove neighboring abdominal organs of the liver which connect to the liver organ, the histogram-based technique is used by finding the left and right histogram tail threshold (HTT) and we repeat the use of morphology filtering and large contour detecting for liver smoothing.

关键词： Computed Tomography liver segmentation hybrid techniques

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Image Based Analysis of Tomato Leaves to Determine the Number of Petioles

Image Based Analysis of Tomato Leaves to Determine the Numbe...

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international conference of soft computing and pattern recognition

作者： Chakraborty, A. K. Sanyal, P. Mukherjee, S. Chatterjee, S. Hazra, P. Bandyopadhyay, S. K. West Bengal Univ Technol Kolkata India B P Poddar Inst Technol Comp Sci & Engn Kolkata India BCKV Dept Vegs & Crops Nadia W Bengal India Univ Calcutta Dept Comp Sci & Engn Kolkata India

ISBN: (纸本)9781424453306

The tomato (Lycopersicon esculentum) is an herbaceous, usually sprawling plant which belong to Solanaceae or nightshade family. Genetic evidence shows that the progenitors of tomatoes were herbaceous green plants with small green *** are a great many (around 7500) tomato varieties grown for various purposes. Their identifications had been studied using various laboratory methods. The morphological and genetical characteristics were employed to classify different tomato cultivars. However, the presence of wide morphological varieties through evolution among various tomato cultivars made it more complex and difficult to classify them. Petioles plays a very crucial role in determining the characteristics of a tomato plant. The number of petioles present, their angle with the leaf stalk or their distance from the stalk represent genetical characteristics which differentiate various cultivars of tomato. This article proposed various methods to find the number of petioles present in a tomato leaf using an image analysis based approach.

关键词： pattern recognition Tomato Cultivars Petioles Image Processing

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Effects of the Use of Multiple Fuzzy Partitions on the Search Ability of Multiobjective Fuzzy Genetics-Based Machine Learning

Effects of the Use of Multiple Fuzzy Partitions on the Searc...

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international conference of soft computing and pattern recognition

作者： Nojima, Yusuke Nakashima, Yusuke Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Naka Ku Osaka 5998531 Japan

ISBN: (纸本)9781424453306

An important issue in the design of fuzzy rule-based systems is to find a good accuracy-complexity tradeoff. While simple fuzzy systems with high interpretability are usually not accurate, complicated fuzzy systems with high accuracy are usually not interpretable. Recently evolutionary multiobjective optimization (EMO) algorithms have been used to search for simple and accurate fuzzy systems. The main advantage of EMO-based approaches over single-objective techniques is that a number of alternative fuzzy systems with different accuracy-complexity tradeoffs can be obtained by their single run. We have already proposed a multiobjective fuzzy genetics-based machine learning (GBML) algorithm for pattern classification problems. In our GBML algorithm, multiple fuzzy partitions with different granularities are simultaneously used. This is because we usually do not know an appropriate fuzzy partition for each input variable. However, the use of multiple fuzzy partitions significantly increases the size of the search space. In this paper, we examine the effect of the use of multiple fuzzy partitions on the search ability of our multiobjective fuzzy GBML algorithms through computational experiments.

关键词： Fuzzy genetics-based machine learning (GBML) Evolutionary multiobjective optimization (EMO) Fuzzy rules Fuzzy partitions pattern classification problems

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Rule Modeling Engine for Optimizing Complex Event Processing patterns

Rule Modeling Engine for Optimizing Complex Event Processing...

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international conference of soft computing and pattern recognition

作者： Behravesh, Babak Shamsuddin, Siti Mariyam Zainal, Anazida Univ Teknol Malaysia Fac Comp Sci & Informat Syst Skudai 81310 Malaysia

ISBN: (纸本)9781424453306

In Complex Event Processing (CEP), we deal with how to search through a sequence of incoming events to find a specified and desired pattern. CEP has a broad use in today enterprise. It can act on sent and/or received events. The result can generate other events that can be used in different layers of an enterprise system. Growing number of areas dealing with arisen events like Business Activity Monitoring (BAM), Fraud detection and intrusion detection makes CEP a hot topic for researchers. Generating efficient high-performance patterns is the issue which has been addressed in this paper. The pattern can be made from any query given by user. The user defined query is CQL (Continuous Query Language) which is relevant for time series data. NFA (Non-deterministic Finite Automaton) is used for modeling patterns although it has some defects which are addressed The focus of this paper is on developing a rule modeling engine and taking into account the role of historical data to make efficient patterns. We developed some algorithms for each component of proposed model. The results are optimized patterns produced based on historical data and queries given by user. Finally we show that these techniques can be efficient when we deal with high volume event-base data.

关键词： Data Mining Association Rules A-priori algorithm Event streams pattern matching Query optimization

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Welcome from the Program Chairs socpar 2009

SoCPaR 2009 - Soft Computing and Pattern Recognition

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socpar 2009 - soft computing and pattern recognition 2009年 xvii页

作者： Muda, Azah Kamilah Ishibuchi, Hisao

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