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共 133 条记录，以下是51-60 订阅

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Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers

Correlation-Based and Causal Feature Selection Analysis for ...

引用

4th workshop on artificial neural networks in pattern recognition

作者： Duangsoithong, Rakkrit Windeatt, Terry Univ Surrey Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

ISBN: (纸本)9783642121586

High dimensional feature spaces with relatively few samples usually leads to poor classifier performance for machine learning, neural networks and data raining systems. This paper presents a comparison analysis between correlation-based and causal feature selection for ensemble classifiers. MLP and SVM are used as base classifier and compared with Naive Bayes and Decision Tree. According to the results, correlation-based feature selection algorithm can eliminate more redundant and irrelevant features, provides slightly better accuracy and less complexity than causal feature selection. Ensemble using Bagging algorithm can improve accuracy in both correlation-based and causal feature selection.

关键词： Correlation-based feature selection causal feature selection ensemble classification

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Parallelized Kernel Patch Clustering

Parallelized Kernel Patch Clustering

引用

4th workshop on artificial neural networks in pattern recognition

作者： Fausser, Stefan Schwenker, Friedhelm Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783642121586

Kernel based clustering methods allow to unsupervised partition samples in feature space but have a. quadratic computation time O(n(2)) where n are the number of samples. Therefore these methods are generally ineligible for large datasets. In this paper we propose a meta-algorithm that performs parallelized clusterings of subsets of the samples and merges them repeatedly. The algorithm is able to use many Kernel based clustering methods where we mainly emphasize on Kernel Fuzzy C-Means and Relational neural Gas. We show that the computation time of this algorithm is basicly linear, i.e. O (n). Further we statistically evaluate the performance of this meta-algorithm on a real-life dataset, namely the Enron Emails.

关键词： Cluster analysis

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Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images

Bayesian Learning of Generalized Gaussian Mixture Models on ...

引用

4th workshop on artificial neural networks in pattern recognition

作者： Elguebaly, Tarek Bouguila, Nizar Concordia Univ Fac Engn & Comp Sci CIISE Montreal PQ H3G 2W1 Canada

ISBN: (纸本)9783642121586

In the context of biomedical image processing and bioinformatics, an important problem is the development of accurate models for image segmentation and DNA spot detection. In this paper we propose a highly efficient unsupervised Bayesian algorithm for biomedical image segmentation and spot detection of cDNA microarray images, based on generalized Gaussian mixture models. Our work is motivated by the fact that biomedical and cDNA microarray images both contain non-Gaussian characteristics, impossible to model using rigid distributions like the Gaussian. Generalized Gaussian mixture models are robust in the presence of noise and outliers and are more flexible to adapt the shape of data.

关键词： Bioinformatics

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SNNS application for crop classification using hymap data

SNNS application for crop classification using hymap data

引用

workshop on Hyperspectral Image and Signal Processing

作者： Olesiuk, Dawid Bachmann, Martin Habermeyer, Martin Heldens, Wieke Zagajewski, Bogdan University of Warsaw Faculty of Geography and Regional Studies WURSEL Poland Germany

ISBN: (纸本)9781424489077

The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are "problematic" because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3x3 pixel sub pattern size - 94,8 %. ©2010 IEEE.

关键词： neural networks

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Immune agent-based neural networks assembly for soft-sensor

Immune agent-based neural networks assembly for soft-sensor

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2nd International workshop on Intelligent Systems and Applications, ISA2010

作者： Shi, Xuhua Qian, Feng Research Institute of Electric Automatic Control NingBo University NingBo China Key Laboratory of Advanced Control and Optimization for Chemical Processes Ecust China University of Science and Technology ShangHai China

ISBN: (纸本)9781424458745

Aiming at difficulty modeling of large amounts of industrial process data, a novel soft sensor model based on artificial immune agent-based multiple model Radial Basis Function (RBF) networks is proposed in this paper. The method is to predict the qualities of manufactured products of Crude Oil Tower. In the IMMST-Team system, some biological immune based operation and learning rules which can efficiently cluster the large amounts of data samples and adapt RBF submodels structure and parameter are established. Individuals in the immune system work cooperatively to accomplish the goal of model training. Meenwhile, immune memory and pattern recognition provide high efficiency of predicting. The prediction of dry point of naphtha produced in a practical industrial process is carried out as a case study. The results obtained indicate that the proposed method provides quality prediction with high efficiency and accuracy, which is capable of learning the relationships between process variables measured during the production cycle. ©2010 IEEE.

关键词： Crude oil

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Immune Agent-Based neural networks Assembly for Soft-Sensor

Immune Agent-Based Neural Networks Assembly for Soft-Sensor

引用

International workshop on Intelligent Systems and Applications, ISA

作者： Xuhua Shi Feng Qian Res. Inst. of Electr. Autom. Control NingBo Univ. Ningbo China Key Lab. of Adv. Control & Optimization for Chem. Processes Ecust China Univ. of Sci. & Technol. Shanghai China

关键词： neural networks Assembly Immune system Biological system modeling artificial neural networks Fuel processing industries Manufacturing industries Biosensors Manufactured products Petroleum

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neural Network Learning Based on Chaotic Imperialist Competitive Algorithm

Neural Network Learning Based on Chaotic Imperialist Competi...

引用

International workshop on Intelligent Systems and Applications, ISA

作者： Marjan Abdechiri Karim Faez Helena Bahrami Department of Electrical Computer & IT Qazvin Azad University Qazvin Iran Department of EE Amirkabir University of Technology슠 Tehran Iran

ISBN: (纸本)9781424458721;9781424458745

Imperialist Competitive Algorithm (ICA) is a novel optimization algorithm that inspired by socio-political process of imperialistic competition. ICA shown its excellent capability in diverse optimization tasks. In this paper, a new method for training an artificial neural Network using Chaotic Imperialist Competitive Algorithm is proposed. In Chaotic Imperialist Competitive Algorithm (CICA) the chaos theory has been used to adjust the movement angle of colonies towards the imperialists. Using Chaotic Imperialist Competitive Algorithm (CICA), the weights of the neural Network in its training phase are updated. In this paper, a multi layer Perceptron neural Network used for prediction of the maximum worth of the stocks change in Tehran's Bourse Market. We trained this neural Network with CICA, ICA, PSO and GA algorithms and compared the experimental results obtained from these four methods. The consideration of results showed that the training and test error of the network trained by CICA algorithm has been reduced in comparison to the other three methods. However, the run time of the proposed algorithm in training the neural network is less than PSO and GA algorithm it is a little more than ICA algorithm.

关键词： neural networks Chaos Independent component analysis artificial neural networks Evolutionary computation Absorption Optimization methods Testing Computer networks pattern recognition

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SNNS application for crop classification using HyMap data

SNNS application for crop classification using HyMap data

引用

workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Dawid Olesiuk Martin Bachmann Martin Habermeyer Wieke Heldens Bogdan Zagajewski Faculty of Geography and Regional Studies WURSEL University of Warmia and Mazury Poland German Remote Sensing Data Center (DFD) German Aerospace Center Germany

The goal of this paper is the presentation of a method and results for artificial neural networks crops classification based on HyMap hyperspectral data. The method that uses an ANNs does not only depend on statistical parameters of particular class and hence makes it possible to include texture information. To experiment with variable pattern size two data sets were chosen with 10 bands obtained after MNF and 5 hyperspectral vegetation indicies. Next to post classification crops maps, additional quality layers were generated to check which classes are “problematic” because of spectral similarity or errors in the training/reference data. The best accuracy was achieved using the 10 MNF bands with the 3×3 pixel sub pattern size -94,8 %.

关键词： Accuracy Agriculture Training artificial neural networks Hyperspectral imaging

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Third generation neural networks: Spiking neural networks

Third generation neural networks: Spiking neural networks

引用

2nd International workshop on Advanced Computational Intelligence, IWACI 2009

作者： Ghosh-Dastidar, Samanwoy Adeli, Hojjat Department of Biomedical Engineering Ohio State University United States Department of Biomedical Engineering Biomedical Informatics Civil and Environmental Engineering Ohio State University 2070 Neil Avenue Columbus OH 43210 United States

ISBN: (纸本)9783642031557

artificial neural networks (ANNs) are based on highly simplified brain dynamics and have been used as powerful computational tools to solve complex pattern recognition, function estimation, and classification problems. Throughout their development, ANNs have been evolving towards more powerful and more biologically realistic models. In the last decade, the third generation Spiking neural networks (SNNs) have been developed which comprise of spiking neurons. Information transfer in these neurons models the information transfer in biological neurons, i.e., via the precise timing of spikes or a sequence of spikes. Addition of the temporal dimension for information encoding in SNNs yields new insight into the dynamics of the human brain and has the potential to result in compact representations of large neural networks. As such, SNNs have great potential for solving complicated time-dependent pattern recognition problems defined by time series because of their inherent dynamic representation. This article presents an overview of the development of spiking neurons and SNNs within the context of feedforward networks, and provides insight into their potential for becoming the next generation neural networks. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Neurons

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Real-time emotion recognition from speech using echo state networks

引用

3rd iapr workshop on artificial neural networks in pattern recognition

作者： Scherer, Stefan Oubbati, Mohamed Schwenker, Friedhelm Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

ISBN: (纸本)9783540699385

The goal of this work is to investigate real-time emotion recognition in noisy environments. Our approach is to solve this problem using novel recurrent neural networks called echo state networks (ESN). ESNs utilizing the sequential characteristics of biologically motivated modulation spectrum features are easy to train and robust towards noisy real world conditions. The standard Berlin Database of Emotional Speech is used to evaluate the performance of the proposed approach. The experiments reveal promising results overcoming known difficulties and drawbacks of common approaches.

关键词： Recurrent neural networks

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