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Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024

Principles of Data Mining (Adaptive Computation and Machine Learning)
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David J. Hand 作者
The MIT Press
譯者
2001-08-01 出版日期
584 頁數
USD 68.00 價格
Hardcover
Adaptive Computation and Machine Learning 叢書系列
9780262082907 圖書編碼

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 圖書標籤: 數據挖掘  datamining  機器學習  textbook  MIT  統計  模式識彆  Statistics   

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發表於2024-06-02

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


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good on overview, and intuition

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good on overview, and intuition

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Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 著者簡介

David Hand是倫敦帝國大學數學係統計學教授。Heikki Mannila是赫爾辛基工業大學計算科學與工程係的教授，諾基亞研究中心的研究員。Padhraic Smyth是加州大學Irvine分校信息與計算科學係的副教授。

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 著者簡介

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Principles of Data Mining (Adaptive Computation and Machine Learning) 在線電子書 圖書描述

The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

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