2006 5th International Conference on Machine Learning and Applications (ICMLA'06)

14-16 Dec. 2006

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  • 5th International Conference on Machine Learning and Applications - Cover

    Publication Year: 2006, Page(s): c1
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  • 5th International Conference on Machine Learning and Applications-Title

    Publication Year: 2006, Page(s):i - iii
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  • 5th International Conference on Machine Learning and Applications-Copyright

    Publication Year: 2006, Page(s): iv
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  • 5th International Conference on Machine Learning and Applications - TOC

    Publication Year: 2006, Page(s):v - viii
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  • Preface

    Publication Year: 2006, Page(s): ix
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  • Conference Committees

    Publication Year: 2006, Page(s): x
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  • Program Committees

    Publication Year: 2006, Page(s): xi
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  • Condition Monitoring Using Pattern Recognition Techniques on Data from Acoustic Emissions

    Publication Year: 2006, Page(s):3 - 9
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (255 KB) | HTML iconHTML

    Condition monitoring applications deploying the usage of impact acoustic techniques are mostly done intuitively by skilled personnel. In this article, a pattern recognition approach is taken to automate such intuitive human skills for the development of more robust and reliable testing methods. The focus of this work is to use the approach as a part of a major research project in the rail inspecti... View full abstract»

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  • A New Machine Learning Technique Based on Straight Line Segments

    Publication Year: 2006, Page(s):10 - 16
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (218 KB) | HTML iconHTML

    This paper presents a new supervised machine learning technique based on distances between points and straight lines segments. Basically, given a training data set, this technique estimates a function where its value is calculated using the distance between points and two sets of straight line segments. A training algorithm has been developed to find these sets of straight line segments that minim... View full abstract»

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  • Horizon Detection Using Machine Learning Techniques

    Publication Year: 2006, Page(s):17 - 21
    Cited by:  Papers (27)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (366 KB) | HTML iconHTML

    Detecting a horizon in an image is an important part of many image related applications such as detecting ships on the horizon, flight control, and port security. Most of the existing solutions for the problem only use image processing methods to identify a horizon line in an image. This results in good accuracy for many cases and is fast in computation. However, for some images with difficult env... View full abstract»

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  • Rule Extraction from Opaque Models-- A Slightly Different Perspective

    Publication Year: 2006, Page(s):22 - 27
    Cited by:  Papers (4)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (307 KB) | HTML iconHTML

    When performing predictive modeling, the key criterion is always accuracy. With this in mind, complex techniques like neural networks or ensembles are normally used, resulting in opaque models impossible to interpret. When models need to be comprehensible, accuracy is often sacrificed by using simpler techniques directly producing transparent models; a tradeoff termed the accuracy vs. comprehensib... View full abstract»

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  • Off-Line Signature Recognition and Verification by Kernel Principal Component Self-Regression

    Publication Year: 2006, Page(s):28 - 33
    Cited by:  Papers (8)  |  Patents (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (251 KB) | HTML iconHTML

    Automatic signature verification is an active area of research with numerous applications such as bank check verification, ATM access, etc. In this research, a kernel principal component self-regression (KPCSR) model is proposed for offline signature verification and recognition problems. Developed from the kernel principal component regression (KPCR), the self-regression model selects a subset of... View full abstract»

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  • Naive Bayes Classification Given Probability Estimation Trees

    Publication Year: 2006, Page(s):34 - 42
    Cited by:  Papers (8)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (358 KB) | HTML iconHTML

    Tree induction is one of the most effective and widely used models in classification. Unfortunately, decision trees such as C4.5 have been found to provide poor probability estimates. By the empirical studies, Provost and Domingos found that probability estimation trees (PETs) give a fairly good probability estimation. However, different from normal decision trees, pruning reduces the performances... View full abstract»

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  • An Efficient Heuristic for Discovering Multiple Ill-Defined Attributes in Datasets

    Publication Year: 2006, Page(s):43 - 47
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (279 KB) | HTML iconHTML

    The accuracy of the rules produced by a concept learning system can be hindered by the presence of errors in the data, such as "ill-defined" attributes that are too general or too specific for the concept to learn. In this paper, we devise a method that uses the Boolean differences computed by a program called Newton to identify multiple ill-defined attributes in a dataset in a single pass. The me... View full abstract»

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  • Lazy Rule Refinement by Knowledge-Based Agents

    Publication Year: 2006, Page(s):48 - 54
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (272 KB) | HTML iconHTML

    This paper presents recent results on developing learning agents that can be taught by subject matter experts how to solve problems, through examples and explanations. It introduces the lazy rule refinement method where the expert modifies an example generated by a learned rule. In this case the agent has to decide whether to modify the rule (if the modification applies to all the previous positiv... View full abstract»

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  • Semi-supervised Data Organization for Interactive Anomaly Analysis.

    Publication Year: 2006, Page(s):55 - 62
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (275 KB) | HTML iconHTML

    We consider the problem of interactive iterative analysis of datasets that consist of a large number of records represented as feature vectors. The record set is known to contain a number of anomalous records that the analyst desires to locate and describe in a short and comprehensive manner The nature of the anomaly is not known in advance (in particular, it is not known, which features or featur... View full abstract»

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  • Incremental Learning By Decomposition

    Publication Year: 2006, Page(s):63 - 68
    Cited by:  Papers (3)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (2089 KB) | HTML iconHTML

    Adaptivity in neural networks aims at equipping learning algorithms with the ability to self-update as new training data becomes available. In many application, data arrives over long periods of time, hence the traditional one-shot training phase cannot be applied. The most appropriate training methodology in such circumstances is incremental learning (IL). The present paper introduces a new IL al... View full abstract»

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  • Market Mechanism Designs with Heterogeneous Trading Agents

    Publication Year: 2006, Page(s):69 - 76
    Cited by:  Papers (5)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (368 KB) | HTML iconHTML

    Market mechanism design research is playing an important role in computational economics for resolving multi-agent allocation problems. A genetic algorithm was used to design auction mechanisms in order to automatically generate a desired market mechanism in agent based E-markets. In previous research, a hybrid market was studied, in which the probability that buyers rather than sellers are able t... View full abstract»

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  • Supernova Recognition Using Support Vector Machines

    Publication Year: 2006, Page(s):77 - 82
    Cited by:  Papers (6)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (391 KB) | HTML iconHTML

    We introduce a novel application of support vector machines (SVMs) to the problem of identifying potential supernovae using photometric and geometric features computed from astronomical imagery. The challenges of this supervised learning application are significant: 1) noisy and corrupt imagery resulting in high levels of feature uncertainty, 2) features with heavy-tailed, peaked distributions, 3)... View full abstract»

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  • On L_1-Norm Multi-class Support Vector Machines

    Publication Year: 2006, Page(s):83 - 88
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (337 KB) | HTML iconHTML

    Binary support vector machines (SVM) have proven effective in classification. However, problems remain with respect to feature selection in multi-class classification. This article proposes a novel multi-class SVM, which performs classification and feature selection simultaneously via L<sub>1</sub>-norm penalized sparse representations. The proposed methodology, together with our devel... View full abstract»

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  • Two-Level Hierarchical Hybrid SVM-RVM Classification Model

    Publication Year: 2006, Page(s):89 - 94
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (330 KB) | HTML iconHTML

    Support vector machines (SVM) and relevance vector machines (RVM) constitute two state-of-the-art learning machines that are currently focus of cutting-edge research. SVM present accuracy and complexity preponderance, but are surpassed by RVM when probabilistic outputs or kernel selection come to discussion. We propose a two-level hierarchical hybrid SVM-RVM model to combine the best of both learn... View full abstract»

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  • Modeling Hesitation and Conflict: A Belief-Based Approach for Multi-class Problems

    Publication Year: 2006, Page(s):95 - 100
    Cited by:  Papers (1)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (3465 KB) | HTML iconHTML

    Support vector machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method do... View full abstract»

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  • Shape Recognition and Retrieval Using String of Symbols

    Publication Year: 2006, Page(s):101 - 108
    Cited by:  Papers (6)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (325 KB) | HTML iconHTML

    In this paper we present two algorithms for shape recognition. Both algorithms map the contour of the shape to be recognized into a string of symbols. The first algorithm is based on supervised learning using string kernels as often used for text categorization and classification. The second algorithm is very weakly supervised and is based on the procrustes analysis and on the edit distance used f... View full abstract»

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  • Intelligent Electronic Navigational Aids: A New Approach

    Publication Year: 2006, Page(s):109 - 116
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (367 KB) | HTML iconHTML

    Intelligent devices, with smart clutter management capabilities, can enhance a user's situational awareness under adverse conditions. Two approaches to assist a user with target detection and clutter analysis are presented, and suggestions on how these tools could be integrated with an electronic chart system are further detailed. The first tool, which can assist a user in finding a target partial... View full abstract»

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  • Application of Reinforcement Learning in Development of a New Adaptive Intelligent Traffic Shaper

    Publication Year: 2006, Page(s):117 - 122
    Cited by:  Papers (2)
    Request permission for reuse | Click to expandAbstract | PDF file iconPDF (491 KB) | HTML iconHTML

    In this paper, we have taken advantage of reinforcement learning to develop a new traffic shaper in order to obtain a reasonable utilization of bandwidth while preventing traffic overload in other part of the network and as a result, reducing total number of packet dropping in the whole network.. We used a modified version of Q-learning in which a combination of neural networks keeps the data of Q... View full abstract»

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