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international conference on Tools for Artificial Intelligence (ICTAI)

作者： Bin Shen Xiaoyuan Su R. Greiner P. Musilek C. Cheng Computing Science University of Alberta Edmonton AB Canada Electrical & Computer Engineering University of Alberta Edmonton AB Canada

Greiner and Zhou (1988) presented ELR, a discriminative parameter-learning algorithm that maximizes conditional likelihood (CL) for a fixed Bayesian belief network (BN) structure, and demonstrated that it often produces classifiers that are more accurate than the ones produced using the generative approach (OFE), which finds maximal likelihood parameters. this is especially true when learning parameters for incorrect structures, such as naive Bayes (NB). In searching for algorithms to learn better BN classifiers, this paper uses ELR to learn parameters of more nearly correct BN structures - e.g., of a general Bayesian network (GBN) learned from a structure-learning algorithm by Greiner and Zhou (2002). While OFE typically produces more accurate classifiers with GBN (vs. NB), we show that ELR does not, when the training data is not sufficient for the GBN structure learner to produce a good model. Our empirical studies also suggest that the better the BN structure is, the less advantages ELR has over OFE, for classification purposes. ELR learning on NB (i.e., with little structural knowledge) still performs about the same as OFE on GBN in classification accuracy, over a large number of standard benchmark datasets.

关键词： Bayesian methods Niobium Computer networks Training data Fault diagnosis machine learning Logistics Frequency estimation pattern recognition data mining

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OCEC: A novel classification algorithm based on evolutionary computation 6

OCEC: A novel classification algorithm based on evolutionary...

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6th international conference on Signal Processing

作者： Liu, J Zhong, WC Liu, F Jiao, LC Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

A novel classification algorithm, OCEC, based on evolutionary computation for data mining is proposed. It is compared to GA-based and non GA-based algorithms on 8 datasets from the UCI machine learning repository. Res... 详细信息

ISBN: (纸本)0780374886

关键词： classification data mining evolutionary computation learning (artificial intelligence) classification algorithm evolutionary computation organizational coevolutionary algorithm data mining UCI machine learning repository prediction accuracy

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An iterative initial-points refinement algorithm for categorical data clustering

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pattern recognition LETTERS 2002年第7期23卷 875-884页

作者： Sun, Y Zhu, QM Chen, ZX Univ Nebraska Dept Comp Sci Digital Imaging & Comp Vis Lab Omaha NE 68182 USA

the original k-means clustering algorithm is designed to work primarily on numeric data sets. this prohibits the algorithm from being directly applied to categorical data clustering in many data mining applications. the k-modes algorithm [Z. Huang, Clustering large data sets with mixed numeric and categorical value, in: Proceedings of the First Pacific Asia Knowledge Discovery and data mining conference. World Scientific, Singapore, 1997, pp. 21-34] extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, as is the case with most data clustering algorithms, the algorithm requires a pre-setting or random selection of initial points (modes) of the clusters. the differences on the initial points often lead to considerable distinct cluster results. In this paper we present an experimental study on applying Bradley and Fayyad's iterative initial-point refinement algorithm to the k-modes clustering to improve the accurate and repetitiveness of the clustering results [cf. P. Bradley, U. Fayyad, Refining initial points for k-mean clustering, in: Proceedings of the 15th international conference on machine learning, Morgan Kaufmann, Los Altos, CA, 1998]. Experiments show that the k-modes clustering algorithm using refined initial points leads to higher precision results much more reliably than the random selection method without refinement, thus making the refinement process applicable to many data mining applications with categorical data. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： data clustering pattern classification refinement algorithm data mining

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Granular neural network for numerical linguistic data fusion 6

Granular neural network for numerical linguistic data fusion

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6th international conference on Signal Processing, ICSP 2002

作者： Xing, Xiao-Shuai Jiao, Li-Chen Key Laboratory for Radar Signal Processing Xidian University Xi'an710071 China Department of Physics Shanxi Teachers University LinFen041004 China

ISBN: (纸本)0780374886

this paper researched a neural networks based knowledge discovery and data mining (KDDM) methodology based on granular computing, neural computing, fuzzy computing, Linguistic computing, and pattern recognition. A granular neural network (GNN) is designed to deal with numerical-Linguistic data, to learn internal granular relations between numerical-Linguistic inputs and outputs, and to deal with numerical-Linguistic data-Fusion, and predict new relation in a database. © 2002 IEEE.

关键词： data mining

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the effect of the Inhibition-Compensation learning Scheme on n-tuple based classifier performance

The effect of the Inhibition-Compensation Learning Scheme on...

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16th international conference on pattern recognition (ICPR)

作者： Hoque, S Fairhurst, MC Guest, RM Univ Kent Dept Elect Canterbury CT2 7NT Kent England

ISBN: (纸本)076951695X

the Inhibition-Compensation learning Scheme (ICLS) has been proposed as a way of enhancing the performance of the Moving Window Classifier In this paper, the effect of ICLS on three n-tuple based classification techniques has been investigated. Pre-segmented handwritten characters from the NIST database have been used as the pattern data. Results show that approximately 2-6% gain in classification accuracy can be achieved in the OCR task domain with no adverse effect on the classification throughput.

关键词： pattern recognition

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5th international conference on Discovery Science, DS 2002

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5th international conference on Discovery Science, DS 2002

ISBN: (纸本)3540001883

the proceedings contain 49 papers. the special focus in this conference is on Application of Discovery to Natural Science, Knowledge Discovery from Unstructured and Semi-structured data. the topics include: Mathematics based on learning;data mining with graphical models;on the eigenspectrum of the gram matrix and its relationship to the operator eigenspectrum;in search of the horowitz factor;learning structure from sequences, with applications in a digital library;discovering frequent structured patterns from string databases;discovery in hydrating plaster using machine learning methods;revising qualitative models of gene regulation;structure extraction using summaries;model complexity and algorithm selection in classification;experiments with projection learning;improved dataset characterisation for meta-learning;racing committees for large datasets;from ensemble methods to comprehensible models;learning the causal structure of overlapping variable sets;extraction of logical rules from data by means of piecewise-linear neural networks;structuring neural networks through bidirectional clustering of weights;toward drawing an atlas of hypothesis classes;datascape survey using the cascade model;learning hierarchical skills from observation;image analysis for detecting faulty spots from microarray images;inferring gene regulatory networks from time-ordered gene expression data using differential equations;modeling state transition of typhoon image sequences by spatio-temporal clustering;structure-sweetness relationships of aspartame derivatives by GUHA;a hybrid approach for Chinese named entity recognition;extraction of word senses from human factors in knowledge discovery;event pattern discovery from the stock market bulletin and email categorization using fast machine learning algorithms.

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Proof running two state-of-the-art pattern recognition techniques in the field of direct marketing 4

Proof running two state-of-the-art pattern recognition techn...

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4th international conference on Enterprise Information Systems, ICEIS 2002

作者： Viaene, Stijn Baesens, Bart Dedene, Guido Vanthienen, Jan Van Den Poel, Dirk Department of Applied Economic Sciences K.U. Leuven Naamsestraat 69 LeuvenB-3000 Belgium Department of Marketing Ghent University Hoveniersberg 24 GhentB-9000 Belgium

ISBN: (纸本)9729805067

In this paper, we synthesize the main findings of three repeat purchase modelling case studies using real-life direct marketing data. Historically, direct marketing - more recently, targeted web marketing - has been one of the most popular domains for the exploration of the feasibility and the viable use of novel business intelligence techniques. Many a data mining technique has been field tested in the direct marketing domain. this can be explained by the (relatively) low-cost availability of recency, frequency, monetary (RFM) and several other customer relationship data, the (relatively) well-developed understanding of the task and the domain, the clearly identifiable costs and benefits, and because the results can often be readily applied to obtain a high return on investment. the purchase incidence modelling cases reported on in this paper were in the first place undertaken to trial run state-of-the-art supervised Bayesian learning multilayer perceptron (MLP) and least squares support vector machine (LS-SVM) classifiers. For each of the cases, we also aimed at exploring the explanatory power (relevance) of the available RFM and other customer relationship related variable operationalizations for predicting purchase incidence in the context of direct marketing.

关键词： Sales

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Rough neurocomputing: A survey of basic models of neurocomputation 3rd

Rough neurocomputing: A survey of basic models of neurocompu...

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3rd international conference on Rough Sets and Current Trends in Computing, RSCTC 2002

作者： Peters, James F. Szczuka, Marcin S. Department of Electrical and Computer Engineering University of Manitoba WinnipegMBR3T 5V6 Canada Institute of Mathematics Warsaw University Banacha 2 Warsaw02-097 Poland

ISBN: (纸本)9783540442745

this article presents a survey of models of rough neurocomputing that have their roots in rough set theory. Historically, rough neurocomputing has three main threads: training set production, calculus of granules, and interval analysis. this form of neurocomputing gains its inspiration from the work of Pawlak on rough set philosophy as a basis for machine learning and from work on data mining and pattern recognition by Swiniarski and others in the early 1990s. this work has led to a variety of new rough neurocomputing computational models that are briefly presented in this article. the contribution of this article is a survey of representative approaches to rough neurocomputing. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Neural networks

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EMG automatic switch for FES control for hemiplegics using artificial neural network

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ROBOTICS AND AUTONOMOUS SYSTEMS 2002年第2-3期40卷 213-224页

作者： Yu, WW Yamaguchi, H Yokoi, H Maruishi, M Mano, Y Kakazu, Y Hokkaido Univ Div Syst & Informat Engn Lab Autonom Syst Engn Sapporo Hokkaido 0608628 Japan Hiroshima Rehabil Ctr Dept Rehabil Hiroshima Japan

Functional Electrical Stimulation (FES) is an effective and developing method to restore functions for paraplegic patients. In this research, we focused on the switching problem of FES, which is one of the obstacles that prevent FES from further practical uses. An adaptive switching for FES control for the lower limbs' activities of hemiplegic patients was developed, based on the consideration that, lower limbs' activities need the synchronization of limbs of both sides. Electromyogram (EMG) signals detected from normal side of hemiplegic patients were used to recognize the activities that the patients intend to do. the recognition results were utilized as the switching signals. However, motion patterns represented and analyzed by EMG are distinctive of individual variations and characteristic alternation, which inevitably result in classification errors in EMG analyzing. To overcome these problems, a feed-forward artificial neural network (ANN) was embedded in an on-line process to form an analyzing system that can adapt to individual characteristics and trace the nonstationary factor. Furthermore, in order to enable the analyzing system to recognize right timings from the EMG-described dynamical processes of activities, such as standing-up and walking, a practical training-set construction method that utilizes additional reference data was proposed. the proposed switching system was applied to a FES system that supports standing-up and walking for a hemiplegics subject, to verify the effectiveness. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： Functional Electrical Stimulation EMG artificial neural network machine learning human-machine interface

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Discriminative learning and informative learning in pattern recognition

Discriminative learning and informative learning in pattern ...

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international conference on Neural Information Processing

作者： Xuechuan Wang K.K. Paliwal School of Microelectronical Engineering Griffith University Brisbane QLD Australia

ISBN: (纸本)9810475241

In pattern recognition, the goal of classification can be achieved from two different types of learning strategy-discriminative teaming and informative learning. Discriminative learning focuses on extracting the discriminative information between classes. Informative learning emphasizes the learning of the class information such as class densities. We review major discriminative learning methods, namely, principal component analysis (PCA), linear discriminant analysis (LDA), minimum classification error (MCE) training algorithm and support vector machine (SVM) and one informative learning method-Gaussian mixture models (GMM). We also discuss the combination of the two types of learning and give the corresponding experiments results.

关键词： pattern recognition Principal component analysis Linear discriminant analysis Support vector machines Support vector machine classification Covariance matrix learning systems Hidden Markov models Australia data mining

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