Feature Selection for Knowledge Discovery and Data Mining: 454 (The Springer International Series in Engineering and Computer Science, 454) - Image 1

£230.05

It has never been this cheap. We have no record of a lower price.

£230 today · cheaper than every other day in the last 3 months

Springer Feature Selection for Knowledge Discovery and Data Mining: 454 (The Springer International Series in Engineering and Computer Science, 454)

Name: Feature Selection for Knowledge Discovery and Data Mining: 454 (The Springer International Series in Engineering and Computer Science, 454)
Brand: Springer
Price: 230.05 GBP
Availability: InStock

AI & Machine Learning

Price Distribution

Price distribution over 28 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £231 (15 days, 53.6%)

Price range: £230 - £231

Price levels: 2 different prices over 28 days

Description

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates.

Product Specifications

Brand: Springer
Format: paperback
ASIN: 1461376041
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Publication Date: 27 January 2013
Listed Since: 20 September 2013

Barcode

No barcode data available

It has never been this cheap. We have no record of a lower price.

Springer Feature Selection for Knowledge Discovery and Data Mining: 454 (The Springer International Series in Engineering and Computer Science, 454)

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Feature Extraction, Construction and Selection: A Data Mining Perspective: 453 (The Springer International Series in Engineering and Computer Science, 453)

Feature Selection for High-Dimensional Data (Artificial Intelligence: Foundations, Theory, and Algorithms)

Feature Selection for Data and Pattern Recognition: 584 (Studies in Computational Intelligence, 584)

Springer Feature Selection for Data and Pattern Recognition

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Computational Methods of Feature Selection (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

Data Preprocessing in Data Mining: 72 (Intelligent Systems Reference Library, 72)

Data Preprocessing in Data Mining: 72 (Intelligent Systems Reference Library, 72)

Dimensionality Reduction in Data Science

Foundations of Computational Intelligence: Volume 4: Bio-Inspired Data Mining: 204 (Studies in Computational Intelligence, 204)

Dirty Data Processing for Machine Learning - Springer

Data Analysis, Classification, and Related Methods (Studies in Classification, Data Analysis, and Knowledge Organization)

Handbook of Statistics: Data Mining and Data Visualization: 0: Volume 24 (Handbook of Statistics, Volume 24)

Meta-Learning in Computational Intelligence: 358

LECTURE NOTES IN DATA MINING

Data Mining With Decision Trees: Theory And Applications (2Nd Edition): 81 (SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE)

RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

No Bullshit Math for Data Science: Your Comprehensive, Short and Sharp Guide to the Mathematics of Data Science

Intelligent Data Mining: Techniques and Applications: 5 (Studies in Computational Intelligence, 5)

It has never been this cheap. We have no record of a lower price.

Springer Feature Selection for Knowledge Discovery and Data Mining: 454 (The Springer International Series in Engineering and Computer Science, 454)

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Feature Extraction, Construction and Selection: A Data Mining Perspective: 453 (The Springer International Series in Engineering and Computer Science, 453)

Feature Selection for High-Dimensional Data (Artificial Intelligence: Foundations, Theory, and Algorithms)

Feature Selection for Data and Pattern Recognition: 584 (Studies in Computational Intelligence, 584)

Springer Feature Selection for Data and Pattern Recognition

Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Computational Methods of Feature Selection (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Spectral Feature Selection for Data Mining (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science

Data Preprocessing in Data Mining: 72 (Intelligent Systems Reference Library, 72)

Data Preprocessing in Data Mining: 72 (Intelligent Systems Reference Library, 72)

Dimensionality Reduction in Data Science

Foundations of Computational Intelligence: Volume 4: Bio-Inspired Data Mining: 204 (Studies in Computational Intelligence, 204)

Dirty Data Processing for Machine Learning - Springer

Data Analysis, Classification, and Related Methods (Studies in Classification, Data Analysis, and Knowledge Organization)

Handbook of Statistics: Data Mining and Data Visualization: 0: Volume 24 (Handbook of Statistics, Volume 24)

Meta-Learning in Computational Intelligence: 358

LECTURE NOTES IN DATA MINING

Data Mining With Decision Trees: Theory And Applications (2Nd Edition): 81 (SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE)

RapidMiner: Data Mining Use Cases and Business Analytics Applications (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)

Artificial Intelligence with Python: A Comprehensive Guide to Building Intelligent Apps for Python Beginners and Developers

No Bullshit Math for Data Science: Your Comprehensive, Short and Sharp Guide to the Mathematics of Data Science

Intelligent Data Mining: Techniques and Applications: 5 (Studies in Computational Intelligence, 5)