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检索条件"任意字段=6th International Conference on Machine Learning and Data Mining in Pattern Recognition"

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Why a window-based learning algorithm using an Effective Boltzmann machine is superior to the original BM learning algorithm

Why a window-based learning algorithm using an Effective Bol...

引用

international conference on Neural Information Processing

作者： M.I. Bellgard R.H. Taplin School of Information Technology Murdoch University Murdoch Australia Department of Mathematics & Statistics Murdoch University Murdoch Australia

Many pattern recognition problems are viewed as problems that can be solved using a window based artificial neural network (ANN). the paper details a unique, window based learning algorithm using the Effective Boltzmann machine (EBM). In the past, EBM, which is based on the Boltzmann machine (BM), has been shown to have the ability to perform pattern completion and to provide an energy measure for completions of any length. Described in the paper is the way that the EBM itself is a highly suitable architecture for learning window based problems. A walk through of a simple example, mathematical derivation as well as simulation experiments shows that the EBM outperforms a window based BM using the criteria of quality of learning, and speed of learning, as well as the resultant generalisations produced by the network.

关键词： machine learning Artificial neural networks Length measurement Information technology Mathematics Statistics Australia pattern recognition Performance evaluation Energy measurement

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Rule generation from a rotation-invariant neural pattern recognition system

Rule generation from a rotation-invariant neural pattern rec...

引用

international conference on Neural Information Processing

作者： M. Fukumi K. Nakaura N. Akamatsu Department of Information Science and Intelligent Systems University of Tokushima Tokushima Japan SYSTEM LSI Limited Matsuyama Japan

A method of extracting rules from a rotation-invariant neural pattern recognition system formed using a genetic algorithm (GA) is presented. In particular, deterministic mutation (DM) is utilized to improve its convergence properties. It is performed on the basis of the result of neural network structure learning. DM can evolve chromosomes of individuals to increase their fitness functions in a deterministic manner. In this paper, coin data are used as inputs. the coins used are a Japanese 500-yen coin and a South Korean 500-won coin, which are very similar. GA is utilized to reduce the number of connection weights in the neural network. the network weights surviving after training represent rules to perform pattern classification for the coin data. the rules are then extracted from the network. Furthermore, the network has a procedure to substitute signum units for hidden sigmoid ones in examining its recognition accuracy. It enables us to easily extract rules. Simulation results show that this approach can generate a simple network structure and, as a result, simple rules for coin data classification.

关键词： pattern recognition Neural networks data mining Delta modulation Genetic mutations Information science Intelligent systems Large scale integration Convergence pattern classification

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Temporal series recognition using a new neural network structure T-CombNET

Temporal series recognition using a new neural network struc...

引用

international conference on Neural Information Processing

作者： M.V. Lamar M. Shoaib Bhuiyan A. Iwata Department of Electrical and Computer Engineering Nagoya Institute of Technology Nagoya Japan Educational Center for Information Processi Nagoya Institute of Technology Nagoya Japan

We present a new neural network structure dedicated to the temporal data series recognition, called T-CombNET (Temporal CombNET). the T-CombNET is a modified and improved version of the CombNET-II structure, which was designed to deal with very large vocabulary character recognition. We incorporated a time normalization stage and used recurrent neural networks to enable CombNET-II to do the temporal analysis. then we applied this new model T-CombNET to the hand gesture recognition task. the preliminary results show that the proposed structure is very efficient, obtaining a recognition rate of 99.41% in visual based kana hand alphabet recognition experiments.

关键词： Neural networks pattern recognition Recurrent neural networks Hidden Markov models data mining Computer networks Information processing Character recognition Weather forecasting Automata

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A diagnostic tool for tree based supervised classification learning algorithms

A diagnostic tool for tree based supervised classification l...

引用

international conference on Neural Information Processing

作者： G. Holmes L. Trigg Dept. of Comput. Sci. Waikato Univ. Hamilton New Zealand Department of Computcr Science University of Waikato Hamilton New Zealand

the process of developing applications of machine learning and data mining that employ supervised classification algorithms includes the important step of knowledge verification. Interpretable output is presented to a user so that they can verify that the knowledge contained in the output makes sense for the given application. As the development of an application is an iterative process it is quite likely that a user would wish to compare models constructed at various times or stages. One crucial stage where comparison of models is important is when the accuracy of a model is being estimated, typically using some form of cross-validation. this stage is used to establish an estimate of how well a model will perform on unseen data. this is vital information to present to a user, but it is also important to show the degree of variation between models obtained from the entire dataset and models obtained during cross-validation. In this way it can be verified that the cross-validation models are at least structurally aligned with the model garnered from the entire dataset. this paper presents a diagnostic tool for the comparison of tree-based supervised classification models. the method is adapted from work on approximate tree matching and applied to decision trees. the tool is described together with experimental results on standard datasets.

关键词： Classification tree analysis Classification algorithms machine learning algorithms machine learning data mining Error analysis Iterative algorithms Decision trees Training data Boosting

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Rule extraction from recurrent neural networks using a symbolic machine learning algorithm

Rule extraction from recurrent neural networks using a symbo...

引用

international conference on Neural Information Processing

作者： A. Vahed C.W. Omlin Dept. of Comput. Sci. Univ. of the Western Cape Bellville South Africa Department of Computer Science University of Stellenbosch South Africa

Addresses the extraction of knowledge from recurrent neural networks trained to behave like deterministic finite-state automata (DFAs). To date, methods used to extract knowledge from such networks have relied on the hypothesis that network states tend to cluster and that clusters of network states correspond to DFA states. the computational complexity of such a cluster analysis has led to heuristics which either limit the number of clusters that may form during training or limit the exploration of the output space of hidden recurrent state neurons. these limitations, while necessary, may lead to reduced fidelity, i.e. the extracted knowledge may not model the true behavior of a trained network, perhaps not even for the training set. the method proposed uses a polynomial-time symbolic learning algorithm to infer DFAs solely from the observation of a trained network's input/output behavior. thus, this method has the potential to increase the fidelity of the extracted knowledge.

关键词： Recurrent neural networks machine learning algorithms Neural networks data mining Africa Information processing Doped fiber amplifiers machine learning Computer science learning automata

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Proceedings if the 1997 IEEE 9th IEEE international conference on Tools with Artificial Intelligence

Proceedings if the 1997 IEEE 9th IEEE International Conferen...

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Proceedings if the 1997 IEEE 9th IEEE international conference on Tools with Artificial Intelligence

the proceedings contains 78 papers from the 1997 IEEE international conference on Tools with Artificial Intelligence. Topics discussed include: neural networks;knowledge representation and reasoning;artificial intelligence;software engineering;genetic algorithms;logic based reasoning systems;natural language processing;vision and pattern recognition;optimization problem solving tools;evolutionary computation;object-oriented methodologies;intelligent agents;knowledge based systems;intelligent user interfaces;data mining;and machine learning.

关键词： Artificial intelligence

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Building multiple prototype classifiers for handwritten character recognition using automatic statistical clustering techniques

Building multiple prototype classifiers for handwritten char...

引用

6th international conference on Image Processing and its Applications

作者： Rahman, AFR Fairhurst, MC Univ Kent Canterbury CT2 7NZ Kent England

ISBN: (纸本)085296692X

Automatic statistical clustering techniques have been applied to implement different multiple prototype classifiers. Multiple prototyping offers an optimised solution to cases where there is significant variability in the training data. A typical application area is the recognition of handwritten characters. Once a set of features has been extracted, different statistical clustering techniques can be implemented to achieve multi-dimensional clustering in the feature space. Building of prototypes from these clusters is straight-forward, the success of the multi-prototyping depends on the efficiency of the statistical clustering techniques. In this paper, different clustering techniques have been used in conjunction with the use of different approaches to the formation of prototypes and the relative performance enhancements are reported.

关键词： pattern recognition systems

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Object tracking in a varying environment

Object tracking in a varying environment

引用

6th international conference on Image Processing and its Applications

作者： Carbonaro, A Zingaretti, P Univ Ancona Ist Informat I-60128 Ancona Italy

ISBN: (纸本)085296692X

An object tracking system based on a template matching approach is demonstrated. the system identifies the target and tracks a sequence of video-recorded images without losing the object by using a genetic algorithm (GA)-based learning to adapt template matching processes to environmental conditions. the adaptive GA generates templates better than fixed, random or indexed template generation techniques in terms of both the cross-correlation score and time necessary to choose the template model. While the overload using the GA is minimal if it is not necessary to change template model, in all other cases the GA offers a better solution to the search the associated search and optimization problem than usual algorithms.

关键词： pattern recognition systems

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Applying backpropagation neural networks to gauging problems within fringe analysis

Applying backpropagation neural networks to gauging problems...

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6th international conference on Image Processing and its Applications

作者： Mills, H Burton, DR Lalor, MJ Liverpool John Moores Univ Liverpool L3 5UX Merseyside England

ISBN: (纸本)085296692X

the backpropagation neural network is applied to three gauging fringe analysis applications: classification of five spherical surfaces of differing radii;classification of five real objects with surfaces of different radii of curvature;and identification of eggs according to their given commercial grades. three methods for creating test and training vectors are used, of which the fast Fourier transform demonstrated drastic reduction in the network size, while applications with relatively noise-free data are indicated for mean/standard deviation driven networks.

关键词： pattern recognition

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learning for feature selection and shape detection 9th

Learning for feature selection and shape detection

引用

9th international conference on Image Analysis and Processing, ICIAP 1997

作者： Cucchiara, Rita Piccardi, Massimo Bariani, Michele Mello, Paola Dipartimento di Ingegneria University of Ferrara via Saragat 1 FerraraI-44100 Italy

ISBN: (纸本)3540635076

the paper proposes a general framework for shape detection based on supervised symbolic learning. Differently from other visual systems exploiting machine learning, the proposed architecture does not follow the object segmentation - feature extraction and (learning based) classification approach. Instead, an initial data-driven processing selects points of interest in the scene by means of complex features which hypothesize the presence of the target shape;hypotheses are validated by a classifier defined by a machine learning algorithm. learning is exploited not only for defining the model, i.e. the description of the target for the classifier, but also for defining the description language, i.e. the feature set useful in generating reliable object hypotheses. the proposed architecture of visual system has been implemented for an industrial application of unstructured shape detection: examples and results are reported in the paper. © Springer-Verlag Berlin Heidelberg 1997.

关键词： learning algorithms

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