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An application of machine learning techniques for the classification of glaucomatous progression 9th

Joint IAPR 9th international Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th international Workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Lazarescu, Mihai Turpin, Andrew Venkatesh, Svetha Department of Computer Science Curtin University GPO Box U1987 Perth6001 Australia

ISBN: (纸本)3540440119

this paper presents an application of machine learning to the problem of classifying patients with glaucoma into one of two classes:stable and progressive glaucoma. the novelty of the work is the use of new features for the data analysis combined with machine learning techniques to classify the medical data. the paper describes the new features and the results of using decision trees to separate stable and progressive cases. Furthermore, we show the results of using an incremental learning algorithm for tracking stable and progressive cases over time. In both cases we used a dataset of progressive and stable glaucoma patients obtained from a glaucoma clinic. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Decision trees

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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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Structural description to recognising Arabic characters using decision tree learning techniques 9th

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Joint IAPR 9th international Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th international Workshop on Statistical Techniques in pattern recognition, SPR 2002

作者： Amin, Adnan School of Computer Science University of New South Wales Sydney2052 Australia

ISBN: (纸本)3540440119

Character recognition systems can contribute tremendously to the advancement of the automation process and can improve the interaction between man and machine in many applications, including office automation, cheque verification and a large variety of banking, business and data entry applications. the main theme of this paper is the automatic recognition of hand-printed Arabic characters using machine learning. Conventional methods have relied on handconstructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. the advantages of machine learning are that it can generalize over the large degree of variation between writing styles and recognition rules can be constructed by example. the system was tested on a sample of handwritten characters from several individuals whose writing ranged from acceptable to poor in quality and the correct average recognition rate obtained using crossvalidation was 87.23%. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Character recognition

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Joint IAPR international Workshops Structural and Syntactic pattern recognition, SSPR 2002 and Statistical Techniques in pattern recognition, SPR 2002

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Joint IAPR 9th international Workshop on Structural and Syntactic pattern recognition, SSPR 2002 and 4th international Workshop on Statistical Techniques in pattern recognition, SPR 2002

ISBN: (纸本)3540440119

the proceedings contain 90 papers. the special focus in this conference is on Graphs, Languages, Strings and Grammars. the topics include: Spectral methods for view-based 3-D object recognition using silhouettes;machine learning for sequential data;graph-based methods for vision;reducing the computational cost of computing approximated median strings;tree k-grammar models for natural language modelling and parsing;algorithms for learning function distinguishable regular languages;non-bayesian graph matching without explicit compatibility calculations;spectral feature vectors for graph clustering;identification of diatoms by grid graph matching;string edit distance, random walks and graph matching;learning structural variations in shock trees;a comparison of algorithms for maximum common subgraph on randomly connected graphs;inexact multisub graph matching using graph eigenspace and clustering models;optimal lower bound for generalized median problems in metric space;structural description to recognising arabic characters using decision tree learning techniques;feature approach for printed document image analysis;example-driven graphics recognition;estimation of texels for regular mosaics using model-based interaction maps;using graph search techniques for contextual colour retrieval;comparing shape and temporal PDMs;linear shape recognition with mixtures of point distribution models;curvature weighted evidence combination for shape-from-shading;probabilistic decisions in production nets;an application of machine learning techniques for the classification of glaucomatous progression;estimating the joint probability distribution of random vertices and arcs by means of second-order random graphs.

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Research of machine learning method for specific information recognition on the Internet 02

Research of machine learning method for specific information...

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4th IEEE international conference on Multimodal Interfaces

作者： Zheng, DQ Hu, Y Zhao, TJ Yu, H Li, S Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China

ISBN: (纸本)0769518346

With the available resources on the Internet becoming plentiful, a large amount of harmful information is permeating in and has been influencing people's normal work and living seriously. therefore, some harmful data stream must be recognized and filtered out effectively. After analyzing some harmful contents in Internet information stream, we present a new method, which recognizes specific information by machine learning (ML). We extracted key information from a number of corpuses through ML method to obtain the part of speech (POS) Transfer-Form for key information by learning from corpuses, which is based on the same pronunciation matching of key information. Further more, the testing value of key information will be obtained in real corpus to examine the likelihood between matching rules from information streams and those learnt from corpuses through the average value of POS transfer probability of key information. therefore, the testing value for the whole real data stream will be obtained the experiment proved that the method was efficient for recognizing certain Internet harmful information.

关键词： learning systems

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Visualization and data mining of high-dimensional data

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2002年第1-2期60卷 147-159页

作者： Inselberg, A Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel Multidimens Graphs Ltd IL-43556 Raanana Israel

Visualization provides insight through images and can be considered as a collection, of application specific mappings: ProblemDomain --> VisualRange. For the visualization of multivariate problems a multidimensional system of parallel coordinates (abbreviated as parallel to -coords) is constructed which induces a one-to-one mapping between subsets of N-space and subsets of 2-space. the result is a rigorous methodology for doing and seeing N-dimensional geometry. Starting with an the overview of the mathematical foundations, it is seen that the display of high-dimensional datasets and search for multivariate relations among the variables is transformed into a 2-D pattern recognition problem. this is the basis for the application to Visual data mining which is illustrated with real dataset of Very Large Scale Integration (VLSI-"chip") production. then a recent geometric classifier is presented and applied to three real datasets. the results compared to those of 23 other classifiers have the least error. the algorithm has quadratic computational complexity in the size and number of parameters, provides comprehensible and explicit rules, does dimensionality selection-where the minimal set of original variables required to state the rule is found-and orders these variables so as to optimize the clarity of separation between the designated set and its complement. Finally, a simple visual economic model of a real country is constructed and analyzed in order to illustrate the special strength of parallel to -coords in modeling multivariate relations by means of hypersurfaces. (C) 2002 Published by Elsevier Science B.V.

关键词： visualization data mining high-dimensional data

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Visualization and data mining of high-dimensional data

Visualization and data mining of high-dimensional data

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4th international conference on Environmetrics and Chemometrics

作者： Inselberg, A Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel Multidimens Graphs Ltd IL-43556 Raanana Israel

关键词： visualization data mining high-dimensional data

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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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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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Emotion recognition of speech based on RNN

Emotion recognition of speech based on RNN

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international conference on machine learning and Cybernetics (ICMLC)

作者： Chang-Hyun Park Dong-Wook Lee Kwee-Bo Sim EEE CAU South Korea

ISBN: (纸本)0780375084

Emotion recognition has various methods. Mainly, it can be performed by visual or aural methods. We know that it is possible to recognize people's emotion by only sound data. We use the pitch of speech as a main feature. We define features of four emotions (normal, angry, laugh, surprise) in pitch analysis. Based on this feature pattern, we implement a simulator using VC++. First of all, this simulator is composed of 'generation of individuals', 'recurrent neural net (RNN)', and 'evaluation'. Using the result from the learning part of this simulator, we can get results applied to other speech data (excepting for learning data). In detail, each module uses the following method. First, the generation of individuals part uses (1+100)-ES and (1+1)-ES (that is, random). thus, we observe the comparison result of both methods. then, we select the best way. Second, the RNN part is composed of 7-nodes. that is, 1 input node, 2 hidden layer nodes, 4 output nodes. Selection of this structure depends on the characteristics of sequentially inputted speech data. third, the evaluation part is very important. this part is the cause of the extraction speed and satisfaction degree of result. then we implement a simulator from the above modules. Applied to other speech data, we observe the result of recognition.

关键词： Emotion recognition Recurrent neural networks Robot sensing systems Neural networks data mining Speech recognition Speech analysis pattern recognition Telephony Positron emission tomography

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