Decision tree induction

To express the visions forged in the workshops to a wide range of data mining researchers and practitioners and foster active participation in the study of foundations of data mining, we edited this volume by involving extended and updated versions of selected papers presented at those workshops as well as some other relevant contributions. The content of this book includes studies of foundations of data mining from theoretical, practical, algorithmical, and managerial perspectives. The following is a brief summary of the papers contained in this book.

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Ebook "Machine learning" includes content: Introduction, concept learning and the general to specific ordering; decision tree learning; artificial neural networks; evaluating hypotheses; bayesian learning; computational learning theory; instance based learning; genetic algorithms; learning sets of rules; analytical learning; combining inductive and analytical learning; reinforcement learning.

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Ebook "Introduction to machine learning" includes content: Preliminaries, boolean functions, using version spaces for learning, neural networks, statistical learning, decision trees, inductive logic programming, computational learning theory, unsupervised learning,... and other contents.

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Lecture Data mining: Lesson 8. The main topics covered in this chapter include: classification and prediction; issues regarding classification and prediction; classification by decision tree induction; scalable decision tree induction; treatment effectiveness analysis;... Please refer to the content of document.

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Lecture Data mining: Lesson 9. The main topics covered in this chapter include: classification and regression; classification by decision tree induction; bayesian classification; other classification methods; regression;... Please refer to the content of document.

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Lecture Data mining: Lesson 10. The main topics covered in this chapter include: classification; classification and regression; classification by decision tree induction; bayesian; classification; other classification methods like rule based, K-NN, SVM, bagging/boosting;... Please refer to the content of document.

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This book "Data mining: Concepts and Techniques (Third edition)" is an extensive and detailed guide to the principal ideas, techniques and technologies of data mining. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book’s coverage of underlying concepts. Please refer to the content of part 2 of book.

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Lecture Artificial Intelligence - Chapter 18: Learning from Observations. The main contents of this chapter include all of the following: Learning agents, inductive learning, decision tree learning, measuring learning performance.

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This chapter presents the following content: Data mining; business analytics; goal, design, techniques & implementation of data mining; decision trees or rule induction, as a knowledge-modeling tool; predictive techniques; real time decision support.

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The single decision tree gives best results for minority classes, cost metric and global accuracy compared with the bagged boosting of trees of the KDDCup’99 winner and classical decision tree algorithms using the Shannon entropy. In contrast to the complex model of KDDCup winner, our decision tree represents inductive rules (IF-THEN) that facilitate human interpretation.

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Learning II: Lecture 21 - Introduction to Artificial Intelligence CS440/ECE448 Inductive learning method, Decision Trees, Learning Decision Trees, How can we do the classification? An ID tree consistent with the data.

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Notes 10: Learning from observations presents about Learning agents, Inductive learning, Decision tree learning, Learning element, Inductive learning method, Learning decision trees, Attribute-based representations.

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Kinds of Learning (Q&A) inductive learning and the acquisition of new knowledge, Come up with some function, Inductive Bias definition, Occam’s Razor, Probably Approximately Correct (PAC) Learning, Version Space.

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Chapter 5: Decision trees Introduction to decision tree; Decision tree for pattern recognition; Construction of decision trees; Splitting at the nodes; Overfitting and pruning; Example of decision tree induction.

30p cocacola_10 08-12-2015 70 2   Download

Data Mining: Classification and Prediction presents about Classification with decision trees; Artificial Neural Networks; Algorithm for decision tree induction; Attribute Selection Measure; Extracting Classification Rules from Trees .

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Thuật toán ID3 Thuật toán ID3 được phát biểu bởi Quinlan (trường đại học Syney, Australia) và được công bố vào cuối thập niên 70 của thế kỷ 20. Sau đó, thuật toán ID3 được giới thiệu và trình bày trong mục Induction on decision trees, machine learning năm 1986.

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In this paper we present a novel, customizable IE paradigm that takes advantage of predicate-argument structures. We also introduce a new way of automatically identifying predicate argument structures, which is central to our IE paradigm. It is based on: (1) an extended set of features; and (2) inductive decision tree learning. The experimental results prove our claim that accurate predicate-argument structures enable high quality IE results.

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Inductive learning or classification of objects from large-scale empirical data sets is an important research area in artificial intelligence (AI). In recent years, many techniques have been developed to perform inductive learning. Among them, the decision tree learning technique is the most popular.

21p balanghuyen 13-01-2010 120 28   Download

Inductive learning or classification of objects from large-scale empirical data sets is an important research area in artificial intelligence (AI). In recent years, many techniques have been developed to perform inductive learning. Among them, the decision tree learning technique is the most popular.

21p balanghuyen 13-01-2010 71 4   Download

RClass*: A Prototype Rough-Set and Genetic Algorithms Enhanced Multi-Concept Classification System for Manufacturing Diagnosis 19.1 Introduction 19.2 Basic Notions 19.3 A Prototype Multi-Concept Classification System 19.4 Validation of RClass * 19.5 Application of RClass * to Manufacturing Diagnosis 19.6 Conclusions 19.1 Introduction Inductive learning or classification of objects from large-scale empirical data sets is an important research area in artificial intelligence (AI). In recent years, many techniques have been developed to perform inductive learning.

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