Dutt, V., & Gonzalez, C.: Making Instance-based Learning Theory Usable and Understand- able: The Instance-based Learning Tool. Computers in Human Behavior, 28(4), 1227-1240. doi: 10.1016/j.chb.2012.02.006, (2012).Dutt, V., & Gonzalez, C.: Making Instance-based Learning T heory ...
INSTANCE-BASED LEARNINGThis paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training...
The algorithms rely primarily on sequential observations rather than a complete domain theory. We report the results of experiments on fixed-length and varying-length sequences. Four sequential similarity metrics are defined and tested: distance, convergence, consistency and recency. Model averaging and...
Learning algorithms Learning Theory Machine Learning Statistical Learning References Aha, D.W. (1989a) Incremental, instance-based learning of independent and graded concept descriptions.Proceed-ings of the Sixth International Workshop on Machine Learning (pp.387-391) Ithaca, NY: Morgan Kaufmann....
As the size of the problems addressed in any Machine Learning task is increasing rapidly, methods to efficiently reduce the number of instances are more important. That is one of the reasons why instance selection is a very active research field both as a general data mining task [7] and ...
关键词: Supervised concept learning incremental learning instance-based concept descriptions learning theory noise similarity DOI: 10.1007/BF00153759 被引量: 7703 年份: 1991 收藏 引用 批量引用 报错 分享 全部来源 免费下载 求助全文 全文购买 Taylor & Francis Springer (全网免费下载) Springer Semantic ...
Experiment 2 was a 10-session (two-week) extended practice study which was conducted to provide a strong test of instance-based learning as a sole mechanism for automaticity. Contrary to predictions of instance theory, the Search and Nonsearch conditions converged for the young adults. Consistent...
RAPR wrote the Python code, selected and trained the deep learning models. AP selected and programmed the Bayesian models and wrote the corresponding theory. VML and JOO wrote the R code, performed the simulations and all the other analyses. VML wrote the initial draft of the manuscript. JOO...
A new method of instance-based learning for databases is proposed. We improve the current similarity measures in several ways using information theory. Similarity is defined on every possible attribute type in a database, and also the weight of each attribute is calculated automatically by the ...
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Statistics for Engineering and Information Science (1999) Google Scholar Lee, H., Ng, A.Y.: Spam Deobfuscation using a Hidden Markov Model. In: Proc. of the Second Conference on E-mail and Anti-Spam CEAS (2005) ...