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An intelligent learning machine

An intelligent learning machine

引用

4th international conference on data mining

作者： Sayad, S Balke, ST Sayad, S Univ Toronto Toronto ON Canada

ISBN: (纸本)1853128066

In this paper we present a new learning system, the "Intelligent learning machine" (ELM). We associate intelligence with the power to learn, forget, grow, contract, interact, and co-operate incrementally, on-line, and in real time. Intelligence in the ILM is based upon the use of a specially customized weight table. the ILM enables parallel data processing and it is well suited to a wide variety of applications and promises unprecedented performance gains in dynamic environments. Here we show how Linear and Non-linear Regression and Classification modeling methods are transformed into intelligent methods. this method has now been successfully software implemented and tested using a variety of databases. Hardware implementation of the ILM is feasible and we foresee an ILM chip for faster computations and mobile applications. Subsequent papers will show how the ELM can be applied to methods such as Bayesian Models, Markov Chain, Hidden Markov Models, Linear Discriminant Analysis, Association Rules, OneR, Principal Component Analysis and Linear Support Vector machines.

关键词： learning systems

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Using emerging patterns and decision trees in rare-class classification

Using emerging patterns and decision trees in rare-class cla...

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4th IEEE international conference on data mining

作者： Alhammady, H Ramamohanarao, K Univ Melbourne Dept Comp Sci & Software Engn Parkville Vic 3052 Australia

ISBN: (纸本)0769521428

the problem of classifying rarely occurring cases is faced in many real life applications. the scarcity of the rare cases makes it difficult to classify them correctly using traditional classifiers. In this paper, we propose a new approach to use emerging patterns (EPs) [3] and decision trees (DTs) in rare-class classification (EPDT). EPs are those itemsets whose supports in one class are significantly higher than their supports in the other classes. EPDT employs the power of EPs to improve the quality of rare-case classification. To achieve this aim, we first introduce the idea of generating new non-existing rare-class instances, and then we over-sample the most important rare-class instances. Our experiments show that EPDT outperforms many classification methods.

关键词： pattern recognition

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Cost-guided class noise handling for effective cost-sensitive learning

Cost-guided class noise handling for effective cost-sensitiv...

引用

4th IEEE international conference on data mining

作者： Zhu, XQ Wu, XD Univ Vermont Dept Comp Sci Burlington VT 05405 USA

ISBN: (纸本)0769521428

Recent research in machine learning, data mining and related areas has produced a wide variety of algorithms for cost-sensitive (CS) classification, where instead of maximizing the classification accuracy, minimizing the misclassfication cost becomes the objective. However, these methods assume that training sets do not contain significant noise, which is rarely the case in real-world environments. In this paper, we systematically study the impacts of class noise on CS learning, and propose a cost-guided class noise handling algorithm to identify noise for effective CS learning. We call it Cost-guided Iterative Classification Filter (CICF), because it seamlessly integrates costs and an existing Classification Filter [1] for noise identification. Instead of putting equal weights to handle noise in all classes in existing efforts, CICF puts more emphasis on expensive classes, which makes it especially successful in dealing with datasets with a large cost-ratio. Experimental results and comparative studies from real-world datasets indicate that the existence of noise may seriously corrupt the performance of CS classifiers, and by adopting the proposed CICF algorithm, we can significantly reduce the misclassfication cost of a CS classifier in noisy environments.

关键词： Spurious signal noise

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learning patterns through artificial contrasts with application to process control

Learning patterns through artificial contrasts with applicat...

引用

4th international conference on data mining

作者： Tuv, E Runger, G Intel Corp Anal & Control Technol Santa Clara CA 95051 USA

ISBN: (纸本)1853128066

In manufacturing as well as other application areas there is a need to learn standard operating conditions in order to detect future changes or deviations. this is related to the even more general problem of detecting instances (cases, records) that are unusual compared to the bulk of the data (outliers). Examples of the problem are fault detection in chemical engineering and statistical process control. the outlier problem is ubiquitous. If specific deviations are not a priori specified, this is a type of unsupervised learning problem. the focus here is on the important, practical case for modem data environments. that is, training data with multiple (usual many) variables of mixed types (without the expedient assumptions common in statistics of multivariate normality that rarely holds in practice). An elegant technique is used to transform an unsupervised leaming problem to a supervised one. this methodology uses an artificial reference distribution. For the focus here such a specific reference distribution requires appropriate properties. then an effective, universal, and nonparametric supervised learner (a gradient boosting machine) is applied to the transformed problem. the results are then in a sense inverted to the original problem. Extensions are mentioned as well as additional insight that becomes available. An illustrative example is presented to justify the validity of this generic and general methodology.

关键词： Process control

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Convex hulls as an hypothesis language bias

Convex hulls as an hypothesis language bias

引用

4th international conference on data mining

作者： Newlands, DA Webb, GI Deakin Univ Geelong Vic 3217 Australia

ISBN: (纸本)1853128066

Classification learning is dominated by systems which induce large numbers of small axis-orthogonal decision surfaces which biases such systems towards particular hypothesis types. However, there is reason to believe that many domains have underlying concepts which do not involve axis orthogonal surfaces. Further, the multiplicity of small decision regions mitigates against any holistic appreciation of the theories produced by these systems, notwithstanding the fact that many of the small regions are individually comprehensible. We propose the use of less strongly biased hypothesis languages which might be expected to model concepts using a number of structures close to the number of actual structures in the domain. An instantiation of such a language, a convex hull based classifier, CH1, has been implemented to investigate modeling concepts as a small number of large geometric structures in n-dimensional space. A comparison of the number of regions induced is made against other well-known systems on a representative selection of largely or wholly continuous valued machine learning tasks. the convex hull system is shown to produce a number of induced regions about an order of magnitude less than well-known systems and very close to the number of actual concepts. this representation, as convex hulls, allows the possibility of extraction of higher level mathematical descriptions of the induced concepts, using the techniques of computational geometry.

关键词： learning systems

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Efficient feature selection for high-dimensional data using two-level filter

Efficient feature selection for high-dimensional data using ...

引用

Proceedings of 2004 international conference on machine learning and Cybernetics

作者： Li, Yun Wu, Zhong-Fu Liu, Jia-Min Tang, Yan-Yun Department of Computer ChongQing University ChongQing 400044 China College of Optics and Electronics ChongQing University ChongQing 400044 China China Light Indust. Mat. Co.Ltd. ChongQing Branch ChongQing 400060 China

ISBN: (纸本)0780384032

Feature selection is a key problem to pattern recognition and machine learning, and it is difficult to get the optimal feature subset for its NP-hand. Currently, the dimensionality of feature set or instance set is very high in many applications, such as information retrieval, so the feature selection from high-dimensional data is also an urgent task for researchers. the paper presents a new approach, which is a two-level filter model system integrateing the ReliefF and a newly developed algorithm of feature cluster, to reduce the dimensionality of large-scale feature set via the feature correlation(relevance) including the feature-feature correlation and feature-class correlation. Our major contributions are: (1) to present a system to perform feature selection from high-dimensional data;(2) to analyze the change of system architecture according to the time cost of the parts in the system;(3) to summarize and comment on the calculations of feature correlation;(4)to perform experiments to show the effective of the proposed approach, which has shown that the system can efficiently get a better compromise between dimensionality reduction and accuracy rate of classification than just part of the system. In many cases, it can improve the accuracy rate and dimensionality reduction.

关键词： data mining

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Collecting and sharing bilingual spontaneous speech corpora: the chinfadial experiment 4

Collecting and sharing bilingual spontaneous speech corpora:...

引用

4th international conference on Language Resources and Evaluation, LREC 2004

作者： Fafiotte, Georges Boitet, Christian Seligman, Mark Zong, Chengqing 385 rue de la Bibliothèque Grenoble Cedex 9F-38041 France National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences P.O.Box 2728 Beijing100080 China

ISBN: (纸本)2951740816

We describe here the three main platforms in the ERIM family of Web-based environments for human interpreting, two of them in more details, ERIM-Interp and ERIM-Collect, then ERIM-Aid. Each platform supports an aspect of the collecting or study of spontaneous bilingual dialogues, translated by an interpreter. ERIM-Interp is the core environment, providing mediated communication between speakers and human interpreters over the network. Using ERIM-Collect, French-Chinese interpreting data have been collected within the 3-year "ChinFaDial" project supported by LIAMA, a French-Chinese laboratory in Beijing. these "raw" speech data will be made available in the spring of 2004 on an open-access basis, using the DistribDial server, on a CLIPSGETA website. Our goal is to extend such corpora, on a collaborative scheme, to allow other research groups to contribute to the site whatever annotations they may have created, and to share them under the same conditions (GPL). An ERIM-Aid variant is intended to provide focused machine AIDS to Web-based human interpreters, or to monolingual distant speakers conversing in different languages.

关键词： Websites

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Relevant linear feature extraction using side-information and unlabeled data

Relevant linear feature extraction using side-information an...

引用

international conference on pattern recognition

作者： Fei Wu Yonglei Zhou Changshui Zhang Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0769521282

"learning with side-information" is attracting more and more attention in machine learning problems. In this paper, we propose a general iterative framework for relevant linear feature extraction. It efficiently utilizes both the side-information and unlabeled data to enhance gradually algorithms' performance and robustness. Both good relevant feature extraction and reasonable similarity matrix estimation can be realized. Specifically, we adopt relevant component analysis (RCA) under this framework and get the derived iterative self-enhanced relevant component analysis (ISERCA) algorithm. the experimental results on several data sets show that ISERCA outperforms RCA.

关键词： Feature extraction Iterative algorithms Robustness Algorithm design and analysis Automation data mining Clustering algorithms machine learning machine learning algorithms Principal component analysis

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Knowledge discovery in hepatitis C virus transgenic mice

引用

17th international conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004

作者： Famili, A. Fazel Ouyang, Junjun Kryworuchko, Marko Alvarez-Maya, Ikuri Smith, Brandon Diaz-Mitoma, Francisco Institute for Information Technology Bldg. M-50 NRC OttawaONK1A0R6 Canada Children’s Hospital of Eastern Ontario 401 Smyth Rd OttawaONK1H 8L1 Canada Institute for Biological Sciences Bldg. M-54 NRC OttawaONK1A0R6 Canada

ISBN: (纸本)3540220070

For the purpose of gene identification, we propose an approach to gene expression data mining that uses a combination of unsupervised and supervised learning techniques to search for useful patterns in the data. the approach involves validation and elimination of irrelevant data, extensive data pre-processing, data visualization, exploratory clustering, pattern recognition and model summarization. We have evaluated our method using data from microarray experiments in a Hepatitis C Virus transgenic mouse model. We demonstrate that from a total of 15311 genes (attributes) we can generate simple models and identify a small number of genes that can be used for future classifications. the approach has potential for future disease classification, diagnostic and virology applications. © Springer-Verlag Berlin Heidelberg 2004.

关键词： data mining

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Neural network-based proper names extraction in fax images

Neural network-based proper names extraction in fax images

引用

international conference on pattern recognition

作者： N. Azzabou L. Likforman-Sulem Signal and Image Processing Laboratory & CNRS LTCI GET/Ecole Nationale Supérieure des Télécommunications Paris France

In this paper, we are interested in the sender's name extraction in fax cover pages through a machine learning scheme. For this purpose, two analysis methods are implemented to work in parallel. the first one is based on image document analysis (OCR recognition, physical block selection), the other on text analysis (word feature extraction, local grammar rules). Our main contribution consisted in introducing a neural network to find an optimal combination of the two approaches. Tests carried on real fax images show that the neural network improves performance compared to an empirical combination function and to each method used separately.

关键词： Neural networks Intelligent networks Image analysis data mining machine learning Text analysis Optical character recognition software Text recognition Feature extraction Electronic mail

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