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An Introduction to Computational Learning Theory
Michael Kearns,Umesh Vazirani +1 more
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TLDR
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata is described.Abstract:
The probably approximately correct learning model Occam's razor the Vapnik-Chervonenkis dimension weak and strong learning learning in the presence of noise inherent unpredictability reducibility in PAC learning learning finite automata by experimentation appendix - some tools for probabilistic analysis.read more
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A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse
TL;DR: It is suggested that within the next few weeks, as well as in the coming months, more details about how to identify the phytochemical properties of cadmium will be released.
Journal ArticleDOI
Using computational learning strategies as a tool for combinatorial optimization
TL;DR: It turns out that the learning strategy can be used as an iterative booster: given a solution to the combinatorial problem, it will start an efficient simulation of a learning algorithm which has a “good chance” to output an improved solution.
Book ChapterDOI
Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm
TL;DR: Energy consumption and energy efficiency are introduced as important factors to consider during data mining algorithm analysis and evaluation and are compared with a theoretical analysis on the Very Fast Decision Tree (VFDT) algorithm.
Revealing Priors on Category Structures Through Iterated Learning
TL;DR: A novel experimental method is presented that makes it possible to directly determine the biases of learners by having people solve a series of inductive problems where the hypothesis selected on one trial is used to generate the data observed on the next.
Proceedings ArticleDOI
Eclectic extraction of propositional rules from Neural Networks
TL;DR: An Eclectic method called HERETIC, which uses Inductive Decision Tree learning combined with information of the neural network structure for extracting logical rules and is shown to be better in terms of speed and performance.