共查询到20条相似文献,搜索用时 31 毫秒
1.
Klaus P. Jantke 《New Generation Computing》1991,8(4):349-360
The main goal is to exhibit the relationship between research work on non-monotonic reasoning and recursion-theoretically based approaches to inductive learning. There are introduced the concepts of monotonic and weakly monotonic inductive inference. It is proved that these concepts are considerably distinguished from other classical concepts of inductive inference, i.e. non-monotonic reasoning is inherently required in several approaches to inductive inference. 相似文献
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A Survey of Methods for Scaling Up Inductive Algorithms 总被引:6,自引:1,他引:5
One of the defining challenges for the KDD research community is to enable inductive learning algorithms to mine very large databases. This paper summarizes, categorizes, and compares existing work on scaling up inductive algorithms. We concentrate on algorithms that build decision trees and rule sets, in order to provide focus and specific details; the issues and techniques generalize to other types of data mining. We begin with a discussion of important issues related to scaling up. We highlight similarities among scaling techniques by categorizing them into three main approaches. For each approach, we then describe, compare, and contrast the different constituent techniques, drawing on specific examples from published papers. Finally, we use the preceding analysis to suggest how to proceed when dealing with a large problem, and where to focus future research. 相似文献
3.
Wu Xindong 《计算机科学技术学报》1993,8(2):22-36
Machine learning(ML)is a major subfield of artificial intelligence(AI).It has been seen as a feasible way of avoiding the knowledge bottleneck problem in knowledge-based systems development.Research on ML has concentrated in the main on inductive learning,a paradigm for inducing rules from unordered sets of exmaples.AQ11 and ID3,the two most widespred algorithms in ML,are both inductive.This paper first summarizes AQ11,ID3 and the newly-developed extension matrix approach based HCV algorithm;and then reviews the recent development of inductive learing and automatic knowledge acquisition from data bases. 相似文献
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Ting-Peng Liang 《Expert systems with applications》1990,1(4):391-401
Inductive learning is a method for automated knowledge acquisition. It converts a set of training data into a knowledge structure. In the process of knowledge induction, statistical techniques can play a major role in improving performance. In this paper, we investigate the competition and integration between the traditional statistical and the inductive learning methods. First, the competition between these two approaches is examined. Then, a general framework for integrating these two approaches is presented. This framework suggests three possible integrations: (1) statistical methods as preprocessors for inductive learning, (2) inductive learning methods as preprocessors for statistical classification, and (3) the combination of the two methods to develop new algorithms. Finally, empirical evidence concerning these three possible integrations are discussed. The general conclusion is that algorithms integrating statistical and inductive learning concepts are likely to make the most improvement in performance. 相似文献
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归纳学习的目的在于发现样例与离散的类之间的映射关系,样例及归纳的映射都需用某个形式化语言描述.归纳学习器采用的形式化语言经历了属性-值语言、一阶逻辑、类型化的高阶逻辑三个阶段,后者能克服前二者在知识表达及学习过程中的很多缺点.本文首先阐述了基于高阶逻辑的复杂结构归纳学习产生的历史背景;其次介绍了基于高阶逻辑的编程语言--Escher的知识描述形式及目前已提出的三种学习方法;复杂结构的归纳学习在机器学习领域的应用及如何解决一些现实问题的讨论随后给出; 最后分析了复杂结构归纳学习的研究所面临的挑战性问题. 相似文献
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作为问题发现和问题解决之间的关键问题与枢纽环节,根因分析目前的研究主要包括基于数据驱动和基于因果驱动两大类方法。鉴于数据驱动方法在缩小根因范围方面具有优势,因而目前根因研究主要聚焦在基于关联规则挖掘、基于启发式搜索、基于机器学习和基于深度学习等数据驱动方法,鲜有从因果知识的角度对根因进行分析,也尚未基于方法维度对根因进行归纳分析研究,缺乏相关研究成果。因此,对近几年根因分析的主要成果进行梳理总结,分析在不同方法维度下根因分析的区别及优势,并提出融合因果知识的根因分析方法,将非对称Shapley值与因果链图相结合以提升根因分析的准确度,最后讨论了现有的研究难点与发展趋势,提出有意义的未来研究方向。 相似文献
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Yonghong Peng 《Journal of Intelligent Manufacturing》2004,15(3):373-380
Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques. 相似文献
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Rolf Wiehagen 《New Generation Computing》1994,12(4):321-335
We present two phenomena which were discovered in pure recursion-theoretic inductive inference, namely inconsistent learning (learing strategies producing apparently “senseless” hypotheses can solve problems unsolvable by “reasonable” learning strategies) and learning from good examples (“much less” information can lead to much more learning power). Recently, it has been shown that these phenomena also hold in the world of polynomial-time algorithmic learning. Thus inductive inference can be understood and used as a source of potent ideas guiding both research and applications in algorithmic learning theory. 相似文献
11.
虽然对归纳逻辑程序的极限行为至今并没有深入的研究,但是通常在分析正在执行的增量式或在线归纳学习算法时,必须考虑这种程序的极限行为.某些归纳学习算法如果不考虑极限行为可能运行到最后会发生错误.如果给定一个递增的例子集合序列,一个归纳逻辑程序会产生一个相应的具有集合论极限的Horn逻辑程序序列,则此归纳逻辑程序是收敛的,并且如果该Horn逻辑程序序列关于例子集合序列的极限是极限正确的,则此归纳逻辑程序是极限正确的,还说明GOLEM系统不是极限正确的.为了解决这个问题,提出了一个极限正确的称为优先GOLEM系统的归纳逻辑系统,并证明了在一定的限制下,优先GOLEM系统的算法是极限正确的. 相似文献
12.
Planning is investigated in an area where classical STRIPS-like approaches usually fail. The application domain is therapy
(i.e. repair) for complex dynamic processes. The peculiarities of this domain are discussed in some detail for convincingly
developing the characteristics of the inductive planning approach presented. Plans are intended to be run for process therapy.
Thus, plans are programs. Because of the unavoidable vagueness and uncertainty of information about complex dynamic processes
in the case of disturbance, therapy plan generation turns out to be inductive program synthesis. There is developed a graph-theoretically
based approach to inductive therapy plan generation. This approach is investigated from the inductive inference perspective.
Particular emphasis is put on consistent and incremental learning of therapy plans. Basic application scenarios are developed
and compared to each other. The inductive inference approach is invoked to develop and investigate a couple of planning algorithms.
The core versions of these algorithms are successfully implemented in Lisp and Prolog.
The work has been partially supported by the German Federal Ministry for Research and Technology (BMFT) within the Joint Project
(BMFT-Verbundprojekt)Wiscon onDevelopment of Methods for Intelligent Monitoring and Control under contract no. 413-4001-01 IW 204 B. Additionally, the second author’s work in learning theory received some support
from the German Federal Ministry for Research and Technology (BMFT) within the Joint Project (BMFT-Verbundprojekt)Gosler onAlgorithmic Learning for Knowledge-Based Systems under contract no. 413-4001-01 IW 101 A.
Oksana Arnold: She graduated from Leipzig University of Technology in 1990 with a Master’s Thesis on a rule interpreter for default reasoning.
She received her PhD. in Computer Science in 1996 on therapy control for complex dynamic processes within a knowledge-based
process supervision and control system. Recently, She works at the University of Leipzig within a research project on information
and communication technologies for virtual enterprises. Her main scientific interest is both in knowledge-based process supervision
and control, where she did a pioneering work on therapy plan generation, and in flexible information systems for new generation
business applications.
Klaus P. Jantke: He graduated from Humboldt University Berlin with a Master’s Thesis in 1975. He received his Ph. D. in Computer Science
in 1979 and his Habilitation at Humboldt in 1984. He worked as the Head of a Research Laboratory in Theoretical Computer Science
and as a Vice-Director of the Computing Center at Humboldt University. Since 1987, Dr. Jantke is full professor at Leipzig
University of Technology. His main research interest is in algorithmic learning theory. Besides this, he contributes to case-based
reasoning, where his special interest is in learning issues and in structural similarity, and to knowledge-based process supervision
and control, especially to planning. Dr. Jantke is member of the ACM, the EATCS, and the GI. 相似文献
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Scaling Up Inductive Learning with Massive Parallelism 总被引:3,自引:0,他引:3
Machine learning programs need to scale up to very large data sets for several reasons, including increasing accuracy and discovering infrequent special cases. Current inductive learners perform well with hundreds or thousands of training examples, but in some cases, up to a million or more examples may be necessary to learn important special cases with confidence. These tasks are infeasible for current learning programs running on sequential machines. We discuss the need for very large data sets and prior efforts to scale up machine learning methods. This discussion motivates a strategy that exploits the inherent parallelism present in many learning algorithms. We describe a parallel implementation of one inductive learning program on the CM-2 Connection Machine, show that it scales up to millions of examples, and show that it uncovers special-case rules that sequential learning programs, running on smaller datasets, would miss. The parallel version of the learning program is preferable to the sequential version for example sets larger than about 10K examples. When learning from a public-health database consisting of 3.5 million examples, the parallel rule-learning system uncovered a surprising relationship that has led to considerable follow-up research. 相似文献
16.
K. A. Naidenova 《Journal of Computer and Systems Sciences International》2009,48(3):401-414
The ways to transform a wide class of machine learning algorithms into processes of plausible reasoning based on known deductive and inductive rules of inference are shown. The employed approach to machine learning problems is based on the concept of a good classification (diagnostic) test for a given set of positive and negative examples. The problem of inferring all good diagnostic tests is to search for the best approximations of the given classification (partition or the partitioning) on the established set of examples. The theory of algebraic lattice is used as a mathematical language to construct algorithms of inferring good classification tests. The advantage of the algebraic lattice is that it is given both as a declarative structure, i.e., the structure for knowledge representation, and as a system of dual operations used to generate elements of this structure. In this work, algorithms of inferring good tests are decomposed into subproblems and operations that are the main rules of plausible human inductive and deductive reasoning. The process of plausible reasoning is considered as a sequence of three mental acts: implementing the rule of reasoning (inductive or deductive)with obtaining a new assertion, refining the boundaries of reasoning domain, and choosing a new rule of reasoning (deductive or inductive one). 相似文献
17.
Inductive logic is a research area in the intersection of machine learning and logic programming, and has been increasingly
applied to data mining. Inductive logic studies learning from examples, within the framework provided by clausal logic. It
provides a uniform and expressive means of representation: examples, background knowledge, and induced theories are all expressed
in first-order logic. Such an expressive representation is computationally expensive, so it is natural to consider improving
the performance of inductive logic data mining using parallelism. We present a parallelization technique for inductive logic,
and implement a parallel version of a core inductive logic programming system: Progol. The technique provides perfect partitioning
of computation and data access and communication requirements are small, so almost linear speedup is readily achieved. However,
we also show why the information flow of the technique permits superlinear speedup over the standard sequential algorithm.
Performance results on several datasets and platforms are reported. The results have wider implications for the design on
parallel and sequential data-mining algorithms.
Received 30 August 2000 / Revised 30 January 2001 / Accepted in revised form 16 May 2001 相似文献
18.
Inductive learning algorithms, in general, perform well on data that have been pre-processed to reduce complexity. By themselves they are not particularly effective in reducing data complexity while learning difficult concepts. Feature construction has been shown to reduce complexity of space spanned by input data. In this paper, we present an iterative algorithm for enhancing the performance of any inductive learning process through the use of feature construction as a pre-processing step. We apply the procedure on three learning methods, namely genetic algorithms, C4.5 and lazy learner, and show improvement in performance. 相似文献
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归纳逻辑程序设计是机器学习与逻辑程序设计交叉所形成的一个研究领域,克服了传统机器学习方法的两个主要限制:即知识表示的限制和背景知识利用的限制,成为机器学习的前沿研究课题。首先从归纳逻辑程序设计的产生背景、定义、应用领域及问题背景介绍了归纳逻辑程序设计系统的概貌,对归纳逻辑程序设计方法的研究现状进行了总结和分析,最后探讨了该领域的进一步的研究方向。 相似文献
20.
Rigel: An Inductive Learning System 总被引:1,自引:0,他引:1