Download or read book Machine Learning ECML 93 written by Pavel B. Brazdil and published by Springer Science & Business Media. This book was released on 1993-03-23 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the Eurpoean Conference on Machine Learning (ECML-93), continuing the tradition of the five earlier EWSLs (European Working Sessions on Learning). The aim of these conferences is to provide a platform for presenting the latest results in the area of machine learning. The ECML-93 programme included invited talks, selected papers, and the presentation of ongoing work in poster sessions. The programme was completed by several workshops on specific topics. The volume contains papers related to all these activities. The first chapter of the proceedings contains two invited papers, one by Ross Quinlan and one by Stephen Muggleton on inductive logic programming. The second chapter contains 18 scientific papers accepted for the main sessions of the conference. The third chapter contains 18 shorter position papers. The final chapter includes three overview papers related to the ECML-93 workshops.
Download or read book Decision Tree Pruning written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-28 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Decision Tree Pruning In machine learning and search algorithms, pruning is a data compression approach that minimizes the size of decision trees by deleting sections of the tree that are non-critical and redundant to classify instances. This reduces the amount of data that has to be stored in the tree. The prediction accuracy is improved as a result of the reduction in overfitting brought about by the use of pruning, which brings about a simplification of the final classifier. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Decision Tree Pruning Chapter 2: Decision Tree Learning Chapter 3: Data Compression Chapter 4: Alpha-Beta Pruning Chapter 5: Null-Move Heuristic Chapter 6: Horizon Effect Chapter 7: Minimum Description Length Chapter 8: Bayesian Network Chapter 9: Ensemble Learning Chapter 10: Artificial Neural Network (II) Answering the public top questions about decision tree pruning. (III) Real world examples for the usage of decision tree pruning in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of decision tree pruning. What is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Download or read book Decision Tree Pruning Using Expert Knowledge written by Jingfeng Cai and published by . This book was released on 2006 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. It has long been a popular artificial intelligence (AI) technique. During the 1980s, it was one of the primary ways for creating an AI system. During the early part of the 1990s, it somewhat fell out of favor, as did the entire AI field in general. However, during the later 1990s, with the emergence of data mining technology, the technique has resurfaced as a powerful method for creating a decision-making program. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has received much less attention than other pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is developed. The intelligent inexact classification is used for first time in KBP1.0 to prune the decision tree. Using expert knowledge in decision tree pruning is discussed for the first time. By comparing the cost-sensitive pruning methods in KBP1.0 with other traditional pruning methods, such as reduced error pruning, pessimistic error pruning, cost complexity pruning, and C4.5, on benchmark data sets, the advantage and disadvantage of cost-sensitive methods in KBP1.0 have been summarized. This research will enhance our understanding of the theory, design and implementation of decision tree pruning using expert knowledge. In the future, the cost-sensitive pruning methods can be integrated into other pruning methods, such as minimum error pruning and critical value pruning, and include new pruning methods in KBP. Using KBP to prune the decision tree and getting the rules from the pruned tree to help us build the expert system is another direction of our future work.
Download or read book Decision Tree Pruning Using Expert Knowledge written by Jingfeng Cai and published by VDM Publishing. This book was released on 2008 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision tree technology has proven to be a valuable way of capturing human decision making within a computer. How to prune the decision tree is one of the research directions of the decision tree technique, but the idea of cost-sensitive pruning has received much less attention than other pruning techniques even though additional flexibility and increased performance can be obtained from this method. This dissertation reports on a study of cost-sensitive methods for decision tree pruning. A decision tree pruning algorithm called KBP1.0, which includes four cost-sensitive methods, is developed. The intelligent inexact classification is used for first time in KBP1.0 to prune the decision tree. Using expert knowledge in decision tree pruning is discussed for the first time. By comparing the cost-sensitive pruning methods in KBP1.0 with other traditional pruning methods on benchmark data sets, the advantage and disadvantage of cost-sensitive methods in KBP1.0 have been summarized. This research will enhance our understanding of the theory, design and implementation of decision tree pruning using expert knowledge.
Download or read book Machine Learning for OpenCV written by Michael Beyeler and published by Packt Publishing Ltd. This book was released on 2017-07-14 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. About This Book Load, store, edit, and visualize data using OpenCV and Python Grasp the fundamental concepts of classification, regression, and clustering Understand, perform, and experiment with machine learning techniques using this easy-to-follow guide Evaluate, compare, and choose the right algorithm for any task Who This Book Is For This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks. What You Will Learn Explore and make effective use of OpenCV's machine learning module Learn deep learning for computer vision with Python Master linear regression and regularization techniques Classify objects such as flower species, handwritten digits, and pedestrians Explore the effective use of support vector machines, boosted decision trees, and random forests Get acquainted with neural networks and Deep Learning to address real-world problems Discover hidden structures in your data using k-means clustering Get to grips with data pre-processing and feature engineering In Detail Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch! Style and approach OpenCV machine learning connects the fundamental theoretical principles behind machine learning to their practical applications in a way that focuses on asking and answering the right questions. This book walks you through the key elements of OpenCV and its powerful machine learning classes, while demonstrating how to get to grips with a range of models.
Download or read book Data Mining and Knowledge Discovery Handbook written by Oded Maimon and published by Springer Science & Business Media. This book was released on 2006-05-28 with total page 1378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Download or read book Data Mining With Decision Trees Theory And Applications 2nd Edition written by Oded Z Maimon and published by World Scientific. This book was released on 2014-09-03 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.This book invites readers to explore the many benefits in data mining that decision trees offer:
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Download or read book Multiple Decision Trees written by Suk Wah Kwok and published by . This book was released on 1988 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Automatic Design of Decision Tree Induction Algorithms written by Rodrigo C. Barros and published by Springer. This book was released on 2015-02-04 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents a detailed study of the major design components that constitute a top-down decision-tree induction algorithm, including aspects such as split criteria, stopping criteria, pruning and the approaches for dealing with missing values. Whereas the strategy still employed nowadays is to use a 'generic' decision-tree induction algorithm regardless of the data, the authors argue on the benefits that a bias-fitting strategy could bring to decision-tree induction, in which the ultimate goal is the automatic generation of a decision-tree induction algorithm tailored to the application domain of interest. For such, they discuss how one can effectively discover the most suitable set of components of decision-tree induction algorithms to deal with a wide variety of applications through the paradigm of evolutionary computation, following the emergence of a novel field called hyper-heuristics. "Automatic Design of Decision-Tree Induction Algorithms" would be highly useful for machine learning and evolutionary computation students and researchers alike.
Download or read book Classification and Regression Trees written by Leo Breiman and published by Routledge. This book was released on 2017-10-19 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Download or read book Decision and Inhibitory Trees and Rules for Decision Tables with Many valued Decisions written by Fawaz Alsolami and published by Springer. This book was released on 2019-03-13 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: The results presented here (including the assessment of a new tool – inhibitory trees) offer valuable tools for researchers in the areas of data mining, knowledge discovery, and machine learning, especially those whose work involves decision tables with many-valued decisions. The authors consider various examples of problems and corresponding decision tables with many-valued decisions, discuss the difference between decision and inhibitory trees and rules, and develop tools for their analysis and design. Applications include the study of totally optimal (optimal in relation to a number of criteria simultaneously) decision and inhibitory trees and rules; the comparison of greedy heuristics for tree and rule construction as single-criterion and bi-criteria optimization algorithms; and the development of a restricted multi-pruning approach used in classification and knowledge representation.
Download or read book A New Decision Tree Pruning Method Based on Rst written by Ming-Yang Wang and published by . This book was released on 2018 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pruning decision tree is an effective method to avoid the phenomena of overfitting. Various pruning methods have been proposed in many literatures. This paper gives a new decision tree pruning method based on Rough Set Theory (RST). According to the concept of explicit region in literature, this paper proposes two new concepts: depth-fitting ratio and error ratio to establish the new pruning strategy. Two thresholds c1 and c2 will be used to control the pruning extent. In addition, this paper gives a concrete example where it compares the new method with Pessimistic Error Pruning (PEP) which has proved the validity of the new one.
Download or read book Hands On Machine Learning with R written by Brad Boehmke and published by CRC Press. This book was released on 2019-11-07 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Download or read book Mathematical Foundations of Big Data Analytics written by Vladimir Shikhman and published by Springer Nature. This book was released on 2021-02-11 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this textbook, basic mathematical models used in Big Data Analytics are presented and application-oriented references to relevant practical issues are made. Necessary mathematical tools are examined and applied to current problems of data analysis, such as brand loyalty, portfolio selection, credit investigation, quality control, product clustering, asset pricing etc. – mainly in an economic context. In addition, we discuss interdisciplinary applications to biology, linguistics, sociology, electrical engineering, computer science and artificial intelligence. For the models, we make use of a wide range of mathematics – from basic disciplines of numerical linear algebra, statistics and optimization to more specialized game, graph and even complexity theories. By doing so, we cover all relevant techniques commonly used in Big Data Analytics.Each chapter starts with a concrete practical problem whose primary aim is to motivate the study of a particular Big Data Analytics technique. Next, mathematical results follow – including important definitions, auxiliary statements and conclusions arising. Case-studies help to deepen the acquired knowledge by applying it in an interdisciplinary context. Exercises serve to improve understanding of the underlying theory. Complete solutions for exercises can be consulted by the interested reader at the end of the textbook; for some which have to be solved numerically, we provide descriptions of algorithms in Python code as supplementary material.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.
Download or read book Modeling and Processing for Next Generation Big Data Technologies written by Fatos Xhafa and published by Springer. This book was released on 2014-11-04 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is intended for researchers, scholars, advanced students, software developers and practitioners working at the forefront in their field.
Download or read book Pruning Decision Trees with Misclassification Costs written by Jeffrey P. Bradford and published by . This book was released on 1998 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
