๐”– Scriptorium
โœฆ   LIBER   โœฆ

๐Ÿ“

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition

โœ Scribed by Ian H. Witten, Eibe Frank, Mark A. Hall


Publisher
Morgan Kaufmann
Year
2011
Tongue
English
Leaves
665
Series
The Morgan Kaufmann Series in Data Management Systems
Edition
3
Category
Library

โฌ‡  Acquire This Volume

No coin nor oath required. For personal study only.

โœฆ Synopsis


Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

โœฆ Subjects


ะ˜ะฝั„ะพั€ะผะฐั‚ะธะบะฐ ะธ ะฒั‹ั‡ะธัะปะธั‚ะตะปัŒะฝะฐั ั‚ะตั…ะฝะธะบะฐ;ะ˜ัะบัƒััั‚ะฒะตะฝะฝั‹ะน ะธะฝั‚ะตะปะปะตะบั‚;ะ˜ะฝั‚ะตะปะปะตะบั‚ัƒะฐะปัŒะฝั‹ะน ะฐะฝะฐะปะธะท ะดะฐะฝะฝั‹ั…;


๐Ÿ“œ SIMILAR VOLUMES


Data Mining: Practical Machine Learning
โœ Ian H. Witten, Eibe Frank ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Morgan Kaufmann ๐ŸŒ English

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al

Data Mining: Practical Machine Learning
โœ Ian H. Witten, Eibe Frank ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Morgan Kaufmann ๐ŸŒ English

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al

Data Mining: Practical Machine Learning
โœ Ian H. Witten, Eibe Frank ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Morgan Kaufmann ๐ŸŒ English

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al

Data Mining: Practical Machine Learning
โœ Ian H. Witten, Eibe Frank ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Morgan Kaufmann ๐ŸŒ English

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al

Data Mining: Practical Machine Learning
โœ Ian H. Witten, Eibe Frank ๐Ÿ“‚ Library ๐Ÿ“… 2005 ๐Ÿ› Morgan Kaufmann ๐ŸŒ English

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no al