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Chinese Conference on pattern recognition/1st CJK Joint workshop on pattern recognition

作者： Hua, Haiyang Zhao, Huaici Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

ISBN: (纸本)9781424441990

Many machine learning algorithms can be applied only to data described by categorical attributes. So discretizatioti of continuous attributes is one of the important steps in preprocessing of extracting knowledge. Traditional discretization algorithms based on clustering need a pre-determined clustering number k, also typically are applied in an unsupervised learning framework. This paper describes such an algorithm, called SX-means (Supervised X-means), which is a new algorithm of supervised discretization of continuous attributes on clustering. The algorithm modifies clusters with knowledge of the class distribution dynamically. And this procedure can not stop until the proper k is found. For the number of clusters k is not pre-determined by the user and class distribution is applied, the random of result is decreased greatly. Experimental evaluation of several discretization algorithms on six artificial data sets show that the proposed algorithm is more efficient and can generate a better discretization schema. Comparing the output of C4.5, resulting tree is smaller, less classification rules, and high accuracy of classification.

关键词： data mining discretization classification supervised clustering

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A dichotomic search algorithm for mining and learning in domain-specific logics

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FUNDAMENTA INFORMATICAE 2005年第1-2期66卷 1-32页

作者： Ferré, S King, RD Univ Rennes 1 IRISA F-35042 Rennes France Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

Many application domains make use of specific data structures such as sequences and graphs to represent knowledge. These data structures are ill-fitted to the standard representations used in machine learning and data-mining algorithms: propositional representations are not expressive enough, and first order ones are not efficient enough. In order to efficiently represent and reason on these data structures, and the complex patterns that are related to them, we use domain-specific logics. We show these logics can be built by the composition of logical components that model elementary data structures. The standard strategies of top-down and bottom-up search are ill-suited to some of these logics, and lack flexibility. We therefore introduce a dichotomic search strategy, that is analogous to a dichotomic search in an ordered array. We prove this provides more flexibility in the search, while retaining completeness and non-redundancy. We present a novel algorithm for learning using domain specific logics and dichotomic search, and analyse its complexity. We also describe two applications which illustrates the search for motifs in sequences;where these motifs have arbitrary length and length-constrained gaps. In the first application sequences represent the trains of the East-West challenge;in the second application they represent the secondary structure of Yeast proteins for the discrimination of their biological functions.

关键词： data structures domain-specific logics search algorithm dichotomy formal concept analysis concept learning pattern mining bioinformatics

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Collaborative scheduling algorithm for full quantity materials based on process and machine learning 24

Collaborative scheduling algorithm for full quantity materia...

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1st international Conference on Image Processing machine learning and pattern recognition

作者： Gu, Sanlin State Grid Gansu Power Supply Co Nanjing Peoples R China

ISBN: (纸本)9798400707032

Participants in the supply chain may have different information, leading to incomplete or inaccurate information when making decisions. To this end, a process and machine learning based collaborative scheduling algorithm for all materials is proposed. Design a health monitoring process for material supply chain based on R-tree dynamic indexing algorithm. Based on this, artificial neural networks in machine learning are applied to mine the data of the entire material supply chain. Through data mining, various data in the supply chain can be integrated and analyzed to improve information transparency and accuracy, and reduce information asymmetry. Adopting a dual layer scheduling model to achieve dual layer collaborative scheduling of materials. The experimental results show that the research method effectively improves the accuracy of data mining in the entire material supply chain, and the utilization rate of materials under this method is always higher than 95%.

关键词： Process design Artificial neural network Full quantity of materials Scheduling algorithm

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Scaling large learning problems with hard parallel mixtures

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international JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2003年第3期17卷 349-365页

作者： Collobert, R Bengio, Y Bengio, S IDIAP CH-1920 Martigny Switzerland Univ Montreal DIRO Montreal PQ Canada

A challenge for statistical learning is to deal with large data sets, e.g. in data mining. The training time of ordinary Support Vector machines is at least quadratic, which raises a serious research challenge if we want to deal with data sets of millions of examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and paxallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becomes easy to learn an "expert" model separately in each region of the partition. A probabilistic extension and the use of a set of generative models allows representing the gater so that all pieces of the model are locally trained. For SVMs, time complexity appears empirically to local growth linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm probably goes down in a cost function that is an upper bound on the negative log-likelihood.

关键词： mixture of experts support vector machines multilayered perceptrons probabilistic models data-mining large-scale learning dimensionality reduction Gaussian mixtures

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machine learning paradigms for utility-based data mining

Machine learning paradigms for utility-based data mining

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1st international workshop on Utility-Based data mining, UBDM '05

作者： Abe, Naoki IBM T. J. Watson Research Center P.O. Box 218 Yorktown Heights NY 10598 United States

ISBN: (纸本)1595932089

关键词： data mining

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Sequential exceptional pattern discovery using pattern-growth: An extensible framework for interpretable machine learning on sequential data 1

Sequential exceptional pattern discovery using pattern-growt...

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1st international workshop on Explainable and Interpretable machine learning, XI-ML 2020

作者： Mollenhauer, Dennis Atzmueller, Martin University of Kassel Wilhelmshöher Allee 73 Kassel34121 Germany Tilburg University Warandelaan 2 Tilburg5037 AB Netherlands

Interpretable machine learning on complex data requires adequate cos-tumizable as well as scalable computational analysis methods. This paper presents a framework combining the paradigms of exceptional model mining with sequential pattern mining in order to mine interesting patterns from complex data. We present a method for sequential exceptional pattern discovery using pattern-growth and further show how to adapt this method for large-scale complex data, with an adaptation to Map/Reduce. First evaluation results demonstrate the efficacy of the proposed method, for both synthetic as well as real-world datasets. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： machine learning

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Neural Networks in MR image estimation from sparsely sampled scans 1st

Neural Networks in MR image estimation from sparsely sampled...

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1st international workshop on machine learning and data mining in pattern recognition

作者： Reczko, M Karras, DA Mertzios, V Graveron-Demilly, D van Ormondt, D Democritus Univ Thrace GR-67100 Xanthi Greece Univ Piraeus Dept Business Adm Athens 16342 Greece Univ Lyon 1 CPE CNRS UPRESA 5012 Lab RMN F-69365 Lyon France Delft Univ Technol Dept Appl Phys NL-2600 GA Delft Netherlands

ISBN: (纸本)3540665994

This paper concerns a novel application of machine learning to Magnetic Resonance Imaging (MRI) by considering Neural Network models for the problem of image estimation from sparsely sampled k-space. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to reduce the measurement time by omitting as many scanning trajectories as possible. This approach, however, entails underdetermined equations and leads to poor image reconstruction. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on machine learning, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving Supervised and Unsupervised Neural Network algorithms for such a problem is considered and it is found that their image reconstruction results are very favorably compared to the ones obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches.

关键词： Image reconstruction

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Nonlinear function learning and classification using optimal radial basis function networks

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2nd international workshop on machine learning and data mining in pattern recognition

作者： Krzyzak, A Concordia Univ Dept Comp Sci Montreal PQ H3G 1M8 Canada

We derive optimal radial kernel in the radial basis function network applied in nonlinear function learning and classification.

ISBN: (纸本)3540423591

We derive optimal radial kernel in the radial basis function network applied in nonlinear function learning and classification.

关键词： Radial basis function networks

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Unsupervised EEG Analysis for Automated Epileptic Seizure Detection 1

Unsupervised EEG Analysis for Automated Epileptic Seizure De...

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1st international workshop on pattern recognition (IWPR)

作者： Birjandtalab, Javad Pouyan, Maziyar Baran Nourani, Mehrdad Univ Texas Richardson Qual Life Technol Lab Richardson TX 75080 USA

ISBN: (数字)9781510604315

ISBN: (纸本)9781510604315

Epilepsy is a neurological disorder which can, if not controlled, potentially cause unexpected death. It is extremely crucial to have accurate automatic pattern recognition and data mining techniques to detect the onset of seizures and inform care-givers to help the patients. EEG signals are the preferred biosignals for diagnosis of epileptic patients. Most of the existing pattern recognition techniques used in EEG analysis leverage the notion of supervised machine learning algorithms. Since seizure data are heavily under-represented, such techniques are not always practical particularly when the labeled data is not sufficiently available or when disease progression is rapid and the corresponding EEG footprint pattern will not be robust. Furthermore, EEG pattern change is highly individual dependent and requires experienced specialists to annotate the seizure and non-seizure events. In this work, we present an unsupervised technique to discriminate seizures and non-seizures events. We employ power spectral density of EEG signals in different frequency bands that are informative features to accurately cluster seizure and non-seizure events. The experimental results tried so far indicate achieving more than 90% accuracy in clustering seizure and non-seizure events without having any prior knowledge on patient's history.

关键词： EEG signals unsupervised seizure detection feature extraction data visualization

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Web usage mining to evaluate the transfer of learning in a web-based learning environment

Web usage mining to evaluate the transfer of learning in a w...

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1st international workshop on Knowledge Discovery and data mining

作者： Chanchary, Farah Habib Haque, Indrani Khalid, Md. Saifuddin Independent Univ Dhaka Bangladesh

ISBN: (纸本)9780769530901

Application of data mining for web log analysis has received significant attention in finding customers' behavioral pattern in e-commerce and learners' behavioral pattern in e-learning. While hit-counts indicate customers' interest in the product or purchasing behavior, a student's visits to a learning Management System (LMS) do not necessarily involve transfer of learning. Addressing such complexity in e-learning, this study analyzed students' log of a learning Management System (LMS) of two subjects at a university in Bangladesh, taught over six weeks duration. data mining and statistical tools have been used to rind relationships between students' LMS access behavior and overall performances. Results show that students having 'Low' access obtained poor grade, on campus access was higher than access from home. Background of students is very important for effective usage of web resources. Majority of the student considered LMS to be a quite helpful tool as teaching-learning method. Preparation and cleaning of the web-log files as well as application of data mining algorithms is important for learners' web usage analysis.

关键词： data mining e-learning

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