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4th International Congress on Sport Sciences Research and Technology Support (icSPORTS)

作者： Wang, Sheng-Wei Hsieh, Wen-Wei Fo Guang Univ Dept Appl Informat 160 Linwei Rd Yilan Taiwan Natl Tsing Hua Univ Off Phys Educ 1Sect 2Kuang Fu Rd Hsinchu Taiwan

ISBN: (纸本)9789897582059

Basketball referees are important in a basketball game. In this paper, we analyze the performance of basketball referees in a game from history data and using the machine learning techniques. The data are collected from Taiwan Super Basketball League games. We first observed that the teamwork is a key factor to the performance of referee teams. Furthermore, the degree of teamwork are more important than the personal capabilities. Then, we derived some classifiers by machine learning algorithms to further analyze the data set. Among the three classifiers, a classifier named linear classifier using pocket algorithm, which is able to classify the data points with most correct rate, performs better than the other two classifiers. The classifier also proved the importance of teamwork is much larger than that of personal capability. In the future, the classifier can be used to predict the performance of a referee team in a basketball game.

关键词： Basketball Referee Performance Analysis Machine Learning pocket algorithm

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On a Ranking Problem Associated with Basel II

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JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2006年第4期5卷 281-289页

作者： Falkowski, Bernd-Juergen Univ Appl Sci Stralsund Dept Econ Zur Schwedenschanze 15 D-18435 Stralsund Germany Victoria Univ Manchester Math Manchester Lancs England Univ Appl Sci Stralsund Sch Econ Business Comp Stralsund Germany

Perceptron learning is discussed in the context of so-called scoring systems used for assessing creditworthiness as stipulated in the Basel II central banks capital accord of the G10-states. The solution of a related ranking problem using a generalised version of the pocket algorithm is described. A correctness proof of the algorithm is given. It is argued that the results obtained may be exploited to compute associated probabilities using a logistic activation function and maximum likelihood methods. Some experimental results concerning an Excel implementation and a Java prototype are exhibited.

关键词： Ranking perceptron learning pocket algorithm Basel II

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Selecting a restoration technique to minimize OCR error

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2003年第3期14卷 478-490页

作者： Cannon, M Fugate, M Hush, DR Scovel, C Los Alamos Natl Lab Comp Res & Applicat Grp Los Alamos NM 87544 USA

This paper introduces a, learning problem related to the of converting printed documents to ASCII text files.. the goal of the learning procedure is to produce a function that maps documents to restoration techniques in such away that on average the restored documents have, minimum optical character recognition-error. We derive a general form. for the optimal function and Use it to, motivate the, development of a nonparametric method based on nearest neighbors. We also develop a direct method, of solution based on empirical error minimization for which we prove a finite sample bound, on. estimation error that. is independent of distribution. We show that this empirical error minimization problem is, an extension of the empirical optimization problem for traditional M-class classification with general loss function and prove computational hardness for! this problem. We then derive a simple iterative algorithm called generalized multiclass. ratchet (GMR) and, prove that it produces, an optimal function asymptotically (with probability, 1). To obtain the GMR algorithm we introduce a new data map, that extends Kesler's construction for the multiclass problem (see, e.g., [5p, 266]) and then apply an algorithm called Ratchet to this mapped data, Where Ratchet is a,modification of the pocket algorithm [6]. Finally;we apply these methods to, a collection of documents and report on the experimental results.

关键词： empirical error minimization estimation error bounds multiclass classification optical character recognition (OCR) perception algorithm pocket algorithm

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On convergence properties of pocket algorithm

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1997年第3期8卷 623-629页

作者： Muselli, M Istituto per i Circuiti Elettronici Consiglio Nationale delle Ricerche Genoa Italy

The problem of finding optimal weights for a single threshold neuron starting from a general training set is considered, Among the variety of possible learning techniques, the pocket algorithm has a proper convergence theorem which asserts its optimality. Unfortunately, the original proof ensures the asymptotic achievement of an optimal weight vector only if the inputs in the training set are integer or rational. This limitation is overcome in this paper by introducing a different approach that leads to the general result, Furthermore, a modified version of the learning method considered, called pocket algorithm with ratchet, is shown to obtain an optimal configuration within a finite number of iterations independently of the given training set.

关键词： convergence theorems neural networks optimal learning perceptron algorithm pocket algorithm threshold neuron

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A NEURAL-NETWORK ARCHITECTURE FOR CLASSIFICATION OF FUZZY INPUTS

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FUZZY SETS AND SYSTEMS 1994年第2期63卷 159-173页

作者： LEE, HM WANG, WT Department of Electronic Egineering National Taiwan Institute of Technology Taipei Taiwan ROC

A neural network for classification problems with fuzzy inputs is proposed. A fuzzy input is represented as an LR-type fuzzy set. A generalized pocket algorithm, called fuzzy pocket algorithm, that uses LR-type fuzzy sets operations and defuzzification method is proposed to train a linear threshold unit (LTU). This LTU node will classify as many fuzzy input instances as possible. Afterward, FV nodes that represent fuzzy vectors will then be generated and expanded, by proposed FVGE learning algorithm, to classify those fuzzy input instances that cannot be classified by the LTU node. The similarity degree between FV nodes and fuzzy inputs is measured by the fuzzy subsethood degree. The network structure is automatically generated. The number of hidden nodes generated depends on the overlapping degree of training instances. Besides, on-line learning is supplied, and parameters used are few and insensitive. The relationship between proposed model and hyperbox-based classifiers, e.g., Fuzzy ART series and Fuzzy Min-Max series, is also discussed. Two sample problems, heart disease and knowledge-based evaluator, are considered to illustrate the working of the proposed model. The experimental results are very

关键词： FUZZY SETS MEMBERSHIP FUNCTION LR-TYPE FUZZY INTERVAL PATTERN RECOGNITION NEURAL NETWORKS pocket algorithm

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DESIGN OF MULTICATEGORY MULTIFEATURE SPLIT DECISION TREES USING PERCEPTRON LEARNING

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PATTERN RECOGNITION 1994年第7期27卷 939-947页

作者： SETHI, IK YOO, JH Vision and Neural Networks Laboratory Department of Computer Science Wayne State University Detroit MI 48202 U.S.A.

The decision tree methodology is an important nonparametric technique for building classifiers from a set of training examples. Most of the existing top-down decision tree design methods make use of single feature splits at successive stages of the tree design. While computationally attractive, single feature splits generally lead to large trees and inferior performance. This paper presents a new top-down decision tree design method that generates compact trees of superior performance by using multifeature splits in place of single feature splits at successive stages of the tree development. The multifeature splits in the proposed method are obtained by combining the concept of information measure of a partition with perceptron learning. Several decision tree induction results for a broad range of classification problems are presented to demonstrate the strengths of the proposed decision tree design methods.

关键词： CLASSIFICATION DECISION TREES MULTIFEATURE SPLITS MUTUAL INFORMATION NEURAL TREES PERCEPTRON LEARNING pocket algorithm

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A CONNECTIONIST LEARNING algorithm WITH PROVABLE GENERALIZATION AND SCALING BOUNDS

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NEURAL NETWORKS 1990年第2期3卷 191-201页

作者： GALLANT, SI Northeastern University USA

A connectionist learning algorithm, the bounded, randomized, distributed (BRD) algorithm, is presented and formally analyzed within the framework of computational learning theory. From a neural network viewpoint this framework gives clear definitions to commonly used terms such as “generalization” and “scaling up,” and addresses the following questions: • • What class of functions is being learned? • • How many training examples should be used? • • How many iterations are required? • • With what certainty can we be assured of learning a good model? From a computational learning theory perspective, a new class of connectionist concepts is shown to be polynomially learnable using the BRD algorithm. Since a variant of the BRD algorithm is in current use for tasks such as pattern recognition, this makes it one of the few learning algorithms shown to be polynomial within the computational learning theory framework that is close to an “industrial strength” algorithm. The algorithm can fail for several reasons: (a) noisy inputs; (b) underestimation of the difficulty of the concept being learned (i.e., larger concept class required); or (c) bad luck. Whenever the algorithm fails, there are several “fallback bounds” available. Finally, the Appendix gives a learnable class of network functions that strictly enlarges a class of learnable functions, Rivest's k-decision lists.

关键词： Connectionist Neural network Computational learning theory PAC-Learning Valiant Model pocket algorithm Distributed method BRD algorithm Perceptron Vapnik-Chervonenkis dimension Decision lists k -order distributed networks

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Modul pro dolování z dat

Modul pro dolování z dat

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作者： Petrlík, Jiří Brno University of Technology

Obsahem této bakalářské práce je seznámení s problematikou dolovaní z dat. Zaměřuji se především na problematiku klasifikace pomocí neuronových sítí. Proto ... 详细信息

Obsahem této bakalářské práce je seznámení s problematikou dolovaní z dat. Zaměřuji se především na problematiku klasifikace pomocí neuronových sítí. Proto zde popisuji některé základní algoritmy pro učení neuronových sítí. Hlavním cílem práce bylo vytvořit nový modul do systému pro dolování z dat, který je vyvíjen na FIT VUT v Brně. Tento systém zde stručně představuji a popisuji zde návrh jeho nového modulu. Výsledný modul jsem otestoval na cvičných datech.

关键词： dolování z dat získávání znalostí z databází klasifikace neuronové sítě upstart algoritmus pocket algoritmus DMSL data mining knowledge discovery in databases classification neural network upstart algorithm pocket algorithm DMSL Text

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