In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. the database used was the Xm2vts database along with...
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Statistical learning problems in many fields involve sequential data. this paper formalizes the principal learning tasks and describes the methods that have been developed within the machinelearning research communit...
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this paper presents an application of machinelearning 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 f...
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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 ...
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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;machi...
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;machinelearning 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 machinelearning techniques for the classification of glaucomatous progression;estimating the joint probability distribution of random vertices and arcs by means of second-order random graphs.
Withthe 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 d...
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ISBN:
(纸本)0769518346
Withthe 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 machinelearning (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.
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 o...
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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.
In this paper, methods of choosing a vehicle out of an image are explored. Digital images are taken from a monocular camera. Image processing techniques are applied to each single frame picture to create the feature v...
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ISBN:
(纸本)0819441937
In this paper, methods of choosing a vehicle out of an image are explored. Digital images are taken from a monocular camera. Image processing techniques are applied to each single frame picture to create the feature vector. Finally the resulting features are used to classify whether there is a car in the picture or not using support vector machines. the result are compared to those obtained using a neural network. A discussion on techniques to enhance the feature vector and th results from bothlearningmachines will be included.
Support vector machines (SVM) are learning algorithms derived from statistical learningtheory. the SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-c...
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Support vector machines (SVM) are learning algorithms derived from statistical learningtheory. the SVM approach was originally developed for binary classification problems. In this paper SVM architectures for multi-class classification problems are discussed, in particular we consider binary trees of SVMs to solve the multi-class problem. Numerical results for different classifiers on a benchmark data set of handwritten digits are presented.
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