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the role of subclasses in machine diagnostics

The role of subclasses in machine diagnostics

引用

15th international conference on pattern recognition, ICPR 2000

作者： Skurichina, Marina Ypma, Alexander Duin, Robert P.W. Pattern Recognition Group Department of Applied Physics Faculty of Applied Sciences Delft University of Technology P.O. Box 5046 Delft2600GA Netherlands

ISBN: (纸本)0769507506

In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some operating modes will usually be missing. In these circumstances, it is important to know which modes (subclasses) are the most valuable for successful machine diagnostics. It is also of interest to investigate the usefulness of noise injection to cover missing operating modes in the data. In this paper, we study the importance of selecting different operating modes of a water-pump and using them for learning in 2-class and 4-class problems. We show that the operating modes representing different running speeds are more valuable than those representing machine loads. We also demonstrate that 2-Nearest Neighbours directed noise injection is useful when filling in missing operating modes in the data. © 2000 IEEE.

关键词： pattern recognition

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data condensation in large databases by incremental learning with support vector machines

Data condensation in large databases by incremental learning...

引用

international conference on pattern recognition

作者： P. Mitra C.A. Murthy S.K. Pal Indian Statistical Institute Machine Intelligence Unit Calcutta India

ISBN: (纸本)0769507506

An algorithm for data condensation using support vector machines (SVM) is presented. the algorithm extracts data points lying close to the class boundaries, which form a much reduced but critical set for classification. the problem of large memory requirements for training SVM in batch mode is circumvented by adopting an active incremental learning algorithm. the learning strategy is motivated from the condensed nearest neighbor classification technique. Experimental results presented show that such active incremental learning enjoy superiority in terms of computation time and condensation ratio, over related methods.

关键词： databases machine learning Support vector machines Support vector machine classification data mining Sampling methods Quadratic programming machine learning algorithms machine intelligence Nearest neighbor searches

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the role of subclasses in machine diagnostics

The role of subclasses in machine diagnostics

引用

international conference on pattern recognition

作者： M. Skurichina A. Ypma R.P.W. Duin Pattern Recognition Group Department of Applied Physics Faculty of Applied Sciences Delft University of Technnology Netherlands

ISBN: (纸本)0769507506

In machine diagnostics it is difficult to collect for learning all possible operating modes of machine functioning. Some of the operating modes are often missing. In these circumstances, it is important to know which modes (subclasses) are the most valuable for successful machine diagnosis. It is also of interest to investigate the usefulness of noise injection to cover the missing operating modes in the data. In this paper, we study the importance of selecting different operating modes of a water-pump and using them for learning in both 2-class and 4-class problems. We show that the operating modes representing different running speeds are more valuable than those representing machine loads. We also demonstrate that the 2-nearest neighbours directed noise injection is useful when filing in missing operating modes in the data.

关键词： Gaussian noise Filling Costs pattern recognition Physics Marine technology Working environment noise machine learning Niobium Scattering

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Behavior analysis of fractal features for texture description in digital images: an experimental study

Behavior analysis of fractal features for texture descriptio...

引用

international conference on pattern recognition

作者： J.J. Valdes L.C. Molina S. Espinosa Department of Languages and Information systems Polytechnic university of Catalonia Barcelona Spain

ISBN: (纸本)0769507506

Texture-based recognition for image segmentation and classification is very important in many domains and different numerical features coming from a variety of approaches have been proposed. Texture segmentation using six features based on the fractal dimension has been used elsewhere. this paper, studies properties of these features from the point of view of dimensionality reduction, mutual relation, differential relevance, discrete quantization, and classification ability. In an experimental framework, a set of statistical, soft computing, data mining and machine learning methods were used on a set of different textures (multidimensional scaling, rough sets, factor analysis, cluster analysis and inductive classification). It was found that fractal features effectively have texture recognition ability. Some of these are very relevant (the fractal dimension of smoothed versions of the original image and the multifractal dimension). Not so many quantisation levels of fractal dimension variables are required in order to achieve high recognition performance.

关键词： Image analysis Image texture analysis Fractals Digital images Image segmentation Quantization data mining Information analysis Information systems Image recognition

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Clustering very large databases using EM mixture models

Clustering very large databases using EM mixture models

引用

international conference on pattern recognition

作者： P.S. Bradley U.M. Fayyad C.A. Reina Microsoft Research Limited China

ISBN: (纸本)0769507506

Clustering very large databases is a challenge for traditional pattern recognition algorithms, e.g. the expectation-maximization (EM) algorithm for fitting mixture models, because of high memory and iteration requirements. Over large databases, the cost of the numerous scans required to converge and large memory requirement of the algorithm becomes prohibitive. We present a decomposition of the EM algorithm requiring a small amount of memory by limiting iterations to small data subsets. the scalable EM approach requires at most one database scan and is based on identifying regions of the data that are discardable, regions that are compressible, and regions that must be maintained in memory. data resolution is preserved to the extent possible based upon the size of the memory buffer and fit of the current model to the data. Computational tests demonstrate that the scalable scheme outperforms similarly constrained EM approaches.

关键词： Clustering algorithms Read-write memory pattern recognition data mining Visual databases Maximum likelihood estimation Probability density function machine learning algorithms Distributed databases Costs

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Approximate clustering in association rules

Approximate clustering in association rules

引用

conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： L.J. Mazlack Computer Science Nippon Steel Information and Communication Systems Inc. Cincinnati OH USA

data mining holds the promise of extracting unsuspected information from very large databases. One difficulty is that discovery techniques are often drawn from methods in which the amount of work increases geometrically with data quantity. Consequentially, the use of these methods is problematic in very large databases. Categorically based association rules are a linearly complex data mining methodology. Unfortunately, rules formed from categorical data often generate many fine grained rules. the concern is how fine grained rules might be aggregated and the role that non-categorical data might have. It appears that soft computing techniques may be useful.

关键词： Association rules data mining data analysis machine learning Computer science Humans Clustering algorithms Spatial databases Telephony pattern recognition

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learning to extract temporal signal patterns from temporal signal sequence

Learning to extract temporal signal patterns from temporal s...

引用

international conference on pattern recognition

作者： Pengyu Hong T.S. Huang Beckman Institute University of Illinois Urbana-Champaign Urbana IL USA

ISBN: (纸本)0769507506

We propose an approach that extracts patterns from a temporal signal sequence without prior knowledge about the lengths, positions and the number of the patterns. Previous research (Hong et al., 1999) proposes a scheme for extracting recurrent patterns from a noise free signal without temporal warping. To handle noise and nonlinear temporal warping, a threshold finite state machine (TFSM) is proposed to perform spatial-temporal data modeling. the TFSM is first roughly initialized. A variance of segmental K-means is used to train the TFSM. the training results give us both the patterns embedding in the signal sequence and the trained TFSM that can be used to represent and detect the patterns.

关键词： Hidden Markov models Multiple signal classification data mining Automata Humans Clustering algorithms Magnetic heads Speech Acoustic signal detection Unsupervised learning

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3D shapes modeling which has hierarchical structure based on B-spline surfaces with non-uniform knots

3D shapes modeling which has hierarchical structure based on...

引用

international conference on pattern recognition

作者： M. Maeda K. Kumamaru K. Inoue H. Zha Kyushu Institute of Technology Kawazu Japan Kyushu University Fukuoka Japan

ISBN: (纸本)0769507506

A method for 3D shapes modeling which has hierarchical structure is proposed. For representing 3D surface shapes, 4th order non-uniform rational B-spline functions with controllable knots are employed as the surface model. Consequently, a multiresolution representation technique based on multiresolution wavelet transform can be implemented with the corresponding B-wavelets. the surface models at each resolution level are obtained by performing a decomposition algorithm, after the surface model at the highest level is estimated. In order to estimate the surface model as accurately as possible, a regularization problem is solved by an iterative algorithm. through several experiments using real data of range images, the effectiveness of the proposed method was confirmed.

关键词： Spline Shape control Surface topography Surface reconstruction Computer vision Multiresolution analysis Wavelet transforms Iterative algorithms Virtual reality data mining

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recognition of hand-printed characters via Induct-RDR 5

Recognition of hand-printed characters via Induct-RDR

引用

5th international conference on Document Analysis and recognition, ICDAR 1999

作者： Amin, Adnan Singh, Sameer School of Computer Science and Engineering University of New South Wales Sydney2052 Australia Department of Computer Science University of Exeter ExeterEX4 4PT United Kingdom

ISBN: (纸本)0769503187

the goal of character recognition research is to simplify and automate the development of character recognition algorithms. We describe an approach based on applying preprocessing to data sets of Latin characters and then applying a machine learning approach to the data sets to build a knowledge base able to classify unseen pre-processed characters. the machine learning method, Induct/RDR, has a number of features that make it particularly suitable for character recognition. It has the potential to integrate automatic analysis with a manual knowledge acquisition methodology if further refinement is required. Initial results on hand-printed Latin characters show the recognition accuracy of up to 90.2% on unseen cases for the machine learning system. © 1999 IEEE.

关键词： Character recognition

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Problem of selecting optimal subset of fuzzy-valued features

Problem of selecting optimal subset of fuzzy-valued features

引用

1999 IEEE international conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics'

作者： Wang, X.Z. Tsang, E.C.C. Yeung, D.S. Hong Kong Polytechnic Univ Kowloon Hong Kong

Feature subset selection refers to a data mining enhancement technique which aims to reduce the number of features to be used. this reduction is expected to improve the performance of data mining algorithms to be used, in aspects of speed, accuracy and simplicity. Although there has been some work on feature subset selection, the research on the theoretically computational complexity of this problem and on the optimal selection of fuzzy-valued feature subsets has not been found. this paper focuses on a problem called Optimal Fuzzy-valued Feature Subset Selection (OFFSS) which is regarded as being important but difficult in machine learning and pattern recognition. the measure of the quality of a set of features is defined by the overall overlapping degree between two classes of examples and the size of feature subset. Main contributions of this paper are that: (1) the concept of fuzzy extension matrix is introduced, (2) the computational complexity of OFFSS is proved to be NP-hard, (3) a simple but powerful heuristic algorithm for OFFSS is given, and (4) the feasibility and simplicity of the proposed algorithm are demonstrated via applications of OFFSS to input selection of neuro-fuzzy systems and the fuzzy decision tree induction.

关键词： learning systems

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