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1. Introduction to Machine Learning
by Ethem Alpaydin
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of ... More
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2. Foundations of Machine Learning
by Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, ... More
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3. Semi-Supervised Learning
by Olivier Chapelle (Editor), Bernhard Scholkopf (Editor), Alexander Zien (Editor)
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art ... More
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4. The Machine Learning: A Probabilistic Perspective
by Kevin P Murphy
Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that ... More
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5. Learning Classifier Systems: From Foundations to Applications
by Pier L Lanzi (Editor), Wolfgang Stolzmann (Editor), Stewart W Wilson (Editor)
Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning ... More
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6. Dataset Shift in Machine Learning
by Joaquin Quinonero-Candela (Editor), Masashi Sugiyama (Editor), Anton Schwaighofer (Editor)
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs ... More
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7. Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation
by Masashi Sugiyama, Motoaki Kawanabe
As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. ... More
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11. Machine Learning Applications in Software Engineering
by Du Zhang (Editor), Jeffrey J P Tsai (Editor), Jeffrey J P Tsai (Editor)
Machine learning deals with the issue of how to build computer programs that improve their performance at some task through experience. Machine ... More
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12. Computational Learning Theory: 4th European Conference, Eurocolt'99 Nordkirchen, Germany, March 29-31, 1999 Proceedings
by Paul Fischer (Editor), European Conference on Computational Lea, Hans Ulrich Simon
This book constitutes the refereed proceedings of the 4th European Conference on Computational Learning Theory, EuroCOLT'99, held in Nordkirchen, ... More
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13. Computational Learning Theory: Third European Conference, Eurocolt '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings
by Shai Ben-David (Editor)
This book constitutes the refereed proceedings of the Third European Conference on Computational Learning Theory, EuroCOLT'97, held in Jerusalem, ... More
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15. Algorithmic Learning Theory: 6th International Workshop, Alt '95, Fukuoka, Japan, October 18 - 20, 1995. Proceedings
by Klaus P Jantke (Editor), Takeshi Shinohara (Editor), Thomas Zeugmann (Editor)
This book constitutes the refereed proceedings of the 6th International Workshop on Algorithmic Learning Theory, ALT '95, held in Fukuoka, Japan, in ... More
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16. Machine Learning and Data Mining for Computer Security: Methods and Applications
by Marcus A. Maloof (Editor)
"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as ... More
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17. Algorithmic Learning Theory - Alt '92: Third Workshop, Alt '92, Tokyo, Japan, October 20-22, 1992. Proceedings
by Shuji Doshita (Editor), Koichi Furukawa (Editor), Klaus P Jantke (Editor)
This volume contains the papers that were presented at the Third Workshop onAlgorithmic Learning Theory, held in Tokyo in October 1992. In addition ... More
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18. Algorithmic Learning Theory: 4th International Workshop, Alt '93, Tokyo, Japan, November 8-10, 1993. Proceedings
by K P Jantke
This volume contains all the papers that were presented at the Fourth Workshop on Algorithmic Learning Theory, held in Tokyo in November 1993. In ... More
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19. Sequence Learning
by Ron Sun (Editor), C L Giles (Editor)
Sequential behavior is essential to intelligence in general and a fundamental part of human activities, ranging from reasoning to language, and from ... More
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20. Machine Learning: Concepts, Methodologies, Tools and Applications (3 Volume Set)
by Irma, Usa Information Resources Management Association (Editor), Information Resources Management Association (Editor)
Machine Learning: Concepts, Methodologies, Tools, and Applications offers a wide-ranging selection of key research in a complex field of study. This ... More
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21. Concept Data Analysis: Theory and Applications
by Claudio Carpineto, Giovanni Romano
With the advent of the Web along with the unprecedented amount of information available in electronic format, conceptual data analysis is more useful ... More
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22. Optimization for Machine Learning
by Suvrit Sra (Editor), Sebastian Nowozin (Editor), Stephen J. Wright (Editor)
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization ... More
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23. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 11th European Conference, Evobio 2013, Vienna, Austria, April 3-5, 2013, Proceedings
by Leonardo Vanneschi (Editor), William S Bush (Editor), Mario Giacobini (Editor)
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25. Machine Beauty: Elegance and the Heart of Computing
by Professor David Hillel Gelernter
Our national character, says David Gelernter, is prejudiced against beautiful technology--a prejudice that has saddled us, in the computer age, with ... More
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