{"product_id":"modern-data-mining-algorithms-in-c-and-cuda-c-recent-developments-in-feature-extraction-and-selection-algorithms-for-data-science-von-timothy-masters","title":"Modern Data Mining Algorithms in C++ and CUDA C","description":"\n                                \n                \u003cp\u003eDiscover 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. \u003c\/p\u003e\n                                \n                \n                \u003cp\u003eAs 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:\u003c\/p\u003e\n                                \n                \u003cul\u003e\n                                        \n                    \u003cli\u003eForward selection component      analysis\u003c\/li\u003e\n                                          \n                    \n                    \u003cli\u003eLocal feature selection\u003c\/li\u003e\n                                        \n                    \u003cli\u003eLinking features and a target      with a hidden Markov model\u003c\/li\u003e\n                                        \n                    \u003cli\u003eImprovements on traditional      stepwise selection\u003c\/li\u003e\n                                        \n                    \u003cli\u003eNominal-to-ordinal      conversion\u003c\/li\u003e\n                                    \n                \u003c\/ul\u003e\n                                 \n                \n                \u003cp\u003eAll algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. \u003c\/p\u003e\n                                \n                \n                \u003cp\u003eThe 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.  \u003c\/p\u003e\n                                \n                \n                \u003cp\u003e\n                                        \n                    \u003cb\u003eWhat You Will Learn\u003c\/b\u003e\n                                    \n                \u003c\/p\u003e\n                                \n                \n                \u003cul\u003e\n                                          \n                    \n                    \u003cli\u003eCombine 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.\u003c\/li\u003e\n                                          \n                    \n                    \u003cli\u003eIdentify 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.\u003c\/li\u003e\n                                          \n                    \n                    \u003cli\u003eFind 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.\u003c\/li\u003e\n                                        \n                    \u003cli\u003eImprove 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.\u003c\/li\u003e\n                                    \n                \u003c\/ul\u003e\n                                 \n                \n                \u003cp\u003e \u003c\/p\u003e\n                                \n                \n                \u003cp\u003e\n                                        \n                    \u003cb\u003eWho This Book Is For\u003c\/b\u003e\n                                         \n                \n                \u003c\/p\u003e\n                                \n                \n                \u003cp\u003eIntermediate to advanced data science programmers and analysts.\u003c\/p\u003e\n                            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781484259870\"\u003e\u003ch3\u003eRecent Developments in Feature Extraction and Selection Algorithms for Data Science\u003c\/h3\u003e\u003c\/div\u003e","brand":"Libri","offers":[{"title":"Softcover - 9781484259870","offer_id":39417104302173,"sku":"9781484259870","price":69.54,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/141099cd-d27f-48f9-9875-23290122bbcf.jpg?v=1775622455","url":"https:\/\/shop.autorenwelt.de\/products\/modern-data-mining-algorithms-in-c-and-cuda-c-recent-developments-in-feature-extraction-and-selection-algorithms-for-data-science-von-timothy-masters","provider":"Autorenwelt Shop","version":"1.0","type":"link"}