共查询到20条相似文献,搜索用时 0 毫秒
1.
Machine Learning on the Basis of Formal Concept Analysis 总被引:12,自引:0,他引:12
S. O. Kuznetsov 《Automation and Remote Control》2001,62(10):1543-1564
A model of machine learning from positive and negative examples (JSM-learning) is described in terms of Formal Concept Analysis (FCA). Graph-theoretical and lattice-theoretical interpretations of hypotheses and classifications resulting in the learning are proposed. Hypotheses and classifications are compared with other objects from domains of data analysis and artificial intelligence: implications in FCA, functional dependencies in the theory of relational data bases, abduction models, version spaces, and decision trees. Results about algorithmic complexity of various problems related to the generation of formal concepts, hypotheses, classifications, and implications. 相似文献
2.
3.
Queries and Concept Learning 总被引:14,自引:2,他引:12
We consider the problem of using queries to learn an unknown concept. Several types of queries are described and studied: membership, equivalence, subset, superset, disjointness, and exhaustiveness queries. Examples are given of efficient learning methods using various subsets of these queries for formal domains, including the regular languages, restricted classes of context-free languages, the pattern languages, and restricted types of propositional formulas. Some general lower bound techniques are given. Equivalence queries are compared with Valiant's criterion of probably approximately correct identification under random sampling. 相似文献
4.
Machine learning deals with the issue of how to build programs that improve their performance at some task through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. They are particularly useful for (a) poorly understood problem domains where little knowledge exists for the humans to develop effective algorithms; (b) domains where there are large databases containing valuable implicit regularities to be discovered; or (c) domains where programs must adapt to changing conditions. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This paper deals with the subject of applying machine learning in software engineering. In the paper, we first provide the characteristics and applicability of some frequently utilized machine learning algorithms. We then summarize and analyze the existing work and discuss some general issues in this niche area. Finally we offer some guidelines on applying machine learning methods to software engineering tasks and use some software development and maintenance tasks as examples to show how they can be formulated as learning problems and approached in terms of learning algorithms. 相似文献
5.
Genetic Algorithms and Machine Learning 总被引:6,自引:0,他引:6
6.
7.
8.
9.
10.
11.
12.
Relevance ranking has been a popular and interesting topic over the years, which has a large variety of applications. A number of machine learning techniques were successfully applied as the learning algorithms for relevance ranking, including neural network, regularized least square, support vector machine and so on. From machine learning point of view, extreme learning machine actually provides a unified framework where the aforementioned algorithms can be considered as special cases. In this paper, pointwise ELM and pairwise ELM are proposed to learn relevance ranking problems for the first time. In particular, ELM type of linear random node is newly proposed together with kernel version of ELM to be linear as well. The famous publicly available dataset collection LETOR is tested to compare ELM-based ranking algorithms with state-of-art linear ranking algorithms. 相似文献
13.
14.
15.
16.
In this letter we present an on-line learning version of the Fokker-Planck machine. The method makes use of a regularized constrained normalized LMS algorithm in order to estimate the time-derivative of the parameter vector of a radial basis function network. The RBF network parametrizes a transition density which satisfies a Fokker-Planck equation, associated to continuous simulated annealing. On-line learning using the constrained normalized LMS method is necessary in order to make the Fokker-Planck machine applicable to large scale nonlinear optimization problems. 相似文献
17.
近年来,机器学习发展迅速,尤其是深度学习在图像、声音、自然语言处理等领域取得卓越成效.机器学习算法的表示能力大幅度提高,但是伴随着模型复杂度的增加,机器学习算法的可解释性越差,至今,机器学习的可解释性依旧是个难题.通过算法训练出的模型被看作成黑盒子,严重阻碍了机器学习在某些特定领域的使用,譬如医学、金融等领域.目前针对机器学习的可解释性综述性的工作极少,因此,将现有的可解释方法进行归类描述和分析比较,一方面对可解释性的定义、度量进行阐述,另一方面针对可解释对象的不同,从模型的解释、预测结果的解释和模仿者模型的解释3个方面,总结和分析各种机器学习可解释技术,并讨论了机器学习可解释方法面临的挑战和机遇以及未来的可能发展方向. 相似文献
18.
19.
Summary Although science can be characterized in terms of search, some search methods let one explore multiple paths in parallel. We have argued that more machine learning researchers should focus their efforts on modeling human behavior, but we have not argued that the field should limit itself to this approach. For those interested in general principles, the study of nonhuman learning methods is also necessary for useful results. In terms of applications, some of machine learning's greatest achievements have involved nonincremental methods that are clearly poor models of human learning. Planes are terrible imitations of birds (and fly less efficiently), but there are still excellent reasons for using aircraft.However, we do believe that too little research has focused on results from the literature on human learning, and that greater attention in this direction would benefit the field as a whole. Science is a complex and bewildering process, and the scientist should employ all available knowledge to direct his steps in useful directions. This strategy seems especially important in young fields like machine learning, in which conflicting views and methods abound. We encourage the reader to join us in applying machine learning techniques to explain the mysteries of human behavior, and in using knowledge of human behavior to constrain our computational theories of learning. 相似文献
20.
机器学习与网络信息处理 总被引:2,自引:0,他引:2
机器学习在网络信息处理中占有重要地位。GHunt是一个采用多项机器学习技术的网络信息智能获取与处理系统。首先,这一系统支持分布式的网络信息并行搜索与内容过滤;其次,采用机器学习技术,包括文本分类、聚类,文本概念抽取,从概念层次理解文本信息;再次,基于概念语义空间有效地统一文本信息管理;最后提供高效的基于概念语义的文本信息检索,以及个性化的专题组织与信息推送服务。文中着重阐述了系统中所用到的机器学习技术。 相似文献