Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees
Somewhat more generally, instance-based learning can refer to a class of procedures for solving new problems based on the solutions of similar past problems. Motivation and Background Most instance-based learning algorithms can be specified by determining the following four items: Distance measure: ...
Instance-based learning algorithms are often faced with the problem of deciding which instances to store for use during generalization. Storing too many in... DR Wilson,TR Martinez - 《Machine Learning》 被引量: 1877发表: 2000年 Advances in Instance Selection for Instance-Based Learning Algorithms...
Instance-based learning algorithms [1][2][3][4][5][6][7] need a distance function in order to determine which instance or instances are closest to a given input vector. The original nearest neighbor algorithm typically uses the Euclidean distance function, which is defined as: E(x, y) ...
Instance reduction algorithms have two main purposes: reduce/eliminate noise and outliers, and reduce the computational burden of instance-based learning algorithms. Regardless of the main objective, the training set should have its number of instances reduced. ...
Algorithms 多实例学习算法两大类: 基于实例的算法(Instance-based) 基于元数据的算法(Metadata-based / Embedding-based) 基于实例的经典算法 迭代判别(Iterative Disambiguation, Dietterich 提出) 阶段一:寻找代表性区域(APR) 阶段二:构建宽松的 APR 多样性密度算法(Diverse Density, Maron 提出) 从MIL 标准假设扩展...
Advances in instance selection for instance-based learning algorithms Data Min. Know. Discov., 6 (2) (2002), pp. 153-172, 10.1023/A:1014043630878 View in ScopusGoogle Scholar [9] J. Calvo-Zaragoza, J.J. Valero-Mas, J.R. Rico-Juan Improving kNN multi-label classification in prototype se...
Here, we comparatively evaluate the genomic predictive performance and informally assess the computational cost of several groups of supervised machine learning methods, specifically, regularized regression methods, deep, ensemble and instance-based learning algorithms, using one simulated animal breeding ...
: Lazy Learning. Kluwer Academic Publishers, Dordrecht (1997) MATH Google Scholar Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Machine Learning 6(1), 37–66 (1991) Google Scholar Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models...
1 Introduction Instance-based learning(IBL) algorithms have shown high classification accura- cies, and its power has been demonstrated in a number of important real world domains (e.g. [2] [5]). As each instance is represented by a set of attribute-value pairs, instance-based learning ...