Modern Data Mining Algorithms in C++ and CUDA C: Recent Developments in Feature

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
subject_code
UMB
gpsr_safety_attestation
true
target_audience
General/trade
is_adult_product
false
edition_number
1
binding
paperback
edition
First
MPN
9,781,484,259,870.00
batteries_required
false
manufacturer
Apress
Brand
Apress
number_of_items
1
pages
240
genre
Data mining
part_number
9781484259870
publication_date
2020-06-06T00:00:01Z
unspsc_code
55101500
batteries_included
false
ISBN
9781484259870
Category

About this product

Product Identifiers

Publisher
Apress L. P.
ISBN-10
1484259874
ISBN-13
9781484259870
eBay Product ID (ePID)
17050097549

Product Key Features

Number of Pages
IX, 228 Pages
Publication Name
Modern Data Mining Algorithms in C++ and CUDA C : Recent Developments in Feature Extraction and Selection Algorithms for Data Science
Language
English
Subject
Programming Languages / General, Probability & Statistics / General, Databases / Data Mining
Publication Year
2020
Type
Textbook
Subject Area
Mathematics, Computers
Author
Timothy Masters
Format
Trade Paperback

Dimensions

Item Weight
16.3 Oz
Item Length
10 in
Item Width
7 in

Additional Product Features

Reviews
"This is an excellent book directed toward those who are already working in data mining." (Anthony J. Duben, Computing Reviews, May 5, 2021)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
1. Introduction.- 2. Forward Selection Component Analysis.- 3. Local Feature Selection.- 4. Memory in Time Series Features.- 5. Stepwise Selection on Steroids.- 6. Nominal-to-Ordinal Conversion.
Synopsis
Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov model Improvements on traditional stepwise selection Nominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets. Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.
LC Classification Number
QA76.9.D343

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