منابع مشابه
Noise-Tolerant Instance-Based Learning Algorithms
Several published reports show that instancebased learning algorithms yield high classification accuracies and have low storage requirements during supervised learning applications. However, these learning algorithms are highly sensitive to noisy training instances. This paper describes a simple extension of instancebased learning algorithms for detecting and removing noisy instances from conce...
متن کاملApplication of the Gabriel Graph to Instance Based Learning Algorithms
.......................................................................................................................... iii Acknowledgements ....................................................................................................... iv Dedication........................................................................................................................ v Table of
متن کاملEffects of domain characteristics on instance-based learning algorithms
This paper presents average-case analyses of instance-based learning algorithms. The algorithms analyzed employ a variant of k-nearest neighbor classi-er (k-NN). Our analysis deals with a monotone m-of-n target concept with irrelevant attributes, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally represent the expected classi-cat...
متن کاملMultiresolution Instance-Based Learning
Instance-based learning methods explicitly remem ber all the data that they receive They usually have no training phase and only at prediction time do they perform computation Then they take a query search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value In this paper we review the advantage...
متن کاملCompositional Instance-Based Learning
This paper proposes a new algorithm for acquisition of preference predicates by a learning apprentice, termed Compositional Instance-Based Learning (CIBL), that permits multiple instances of a preference predicate to be composed, directly exploiting the transitivity of preference predicates. In an empirical evaluation, CIBL was consistently more accurate than a I-NN instance-based learning stra...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 1991
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00153759