Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
Language: English
Published by Apress, 2020
- Softcover
- New
Condition: New
£ 48.37
Quantity: Over 20 available
Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A novel expert-driven data-mining approach to algorithms in C++ and CUDA C Author has been developing and using algorithms for over 20 yearsData mining is an important topic in big data and data science.
Seller Inventory # 362611029
- Title
- Modern Data Mining Algorithms in C++ and Cuda C: Recent Developments in Feature Extraction and Selection Algorithms for Data Science
- Author
- Timothy Masters
- Publisher
- Apress
- Publication year
- 2020
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 1484259874
- ISBN 13
- 9781484259870
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.
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 16 to 45 business days | 16 to 45 business days |
|---|---|---|
| First item | £ 40.83 | £ 40.83 |
Payment methods
- Bank Wire Transfer
- Check
- Paypal
Store description
Online Handel nur mit Neubüchern
Seller's business information
Moluna GmbH
Engberdingdamm 27
Greven, Germany 48268
Terms of sale
About Us
Legal website operator identification:
Moluna GmbH
Represented by the general manager Helge Blischke
Engberdingdamm 27
48268 Greven
Germany
Telephone: 02571/5698933
Telefax: 02571/5698930
E-Mail: abe@moluna.de
VAT No.: DE296281834
listed in the commercial register of the local court Steinfurt
Commercial register number - Part B of the commercial register - 10553
We are a member of the initiative „FairCommerce“ since 24.07.2015.
For more information, see: www.haendlerbund.de/faircommerce.
Right of withdrawal
If you are a consumer you can withdraw from the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
Information regarding the right of withdrawal
Statutory right to withdraw
You have the right to withdraw from this contract within 14 days without giving any reason.
The withdrawal period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the last good or the last lot or piece.
To exercise the right of withdrawal, electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". We will communicate to you an acknowledgement of receipt of such a withdrawal on a durable medium (e.g. by e-mail) without delay.
To meet the withdrawal deadline, it is sufficient for you to send your communication concerning your exercise of the right of withdrawal before the withdrawal period has expired.
Effects of withdrawal
If you withdraw from this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to withdraw from this contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back, or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to moluna, Greven, Germany, without undue delay and in any event not later than 14 days from the day on which you communicate your withdrawal from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of withdrawal
The right of withdrawal does not apply to:
- The delivery of newspapers, journals or magazines with the exception of subscription contracts; and
- The supply of digital content which is not supplied on a tangible medium (e.g. on a CD or DVD) if you accepted when you placed your order that we could start to deliver it, and that you could not withdraw once delivery had started.