{"product_id":"data-mining-algorithms-in-c-von-timothy-masters","title":"Data Mining Algorithms in C++","description":"\n                                Discover hidden relationships among the variables in your data, and learn how to exploit these relationships.  This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications.  All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code.\n                \n                \u003cbr\u003e\n                                Many of these techniques are recent developments, still not in widespread use.  Others are standard algorithms given a fresh look.  In every case, the focus is on practical applicability, with all code written in such a way that it can easily be included into any program.  The Windows-based DATAMINE program lets you experiment with the techniques before incorporating them into your own work.\n                \n                \u003cbr\u003e\n                                \n                \u003cb\u003eWhat You'll Learn\u003c\/b\u003e\n                                \n                \u003cul\u003e\n                                        \n                    \u003cli\u003e\n                                                Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data\n                        \n                        \u003cbr\u003e\n                                            \n                    \u003c\/li\u003e\n                                        \n                    \u003cli\u003e\n                                                Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data\n                        \n                        \u003cbr\u003e\n                                            \n                    \u003c\/li\u003e\n                                        \n                    \u003cli\u003e\n                                                Work with feature weighting as regularized energy-based learning to rank variables according to their predictive power when there is too little data for traditional methods\n                        \n                        \u003cbr\u003e\n                                            \n                    \u003c\/li\u003e\n                                        \n                    \u003cli\u003e\n                                                See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data\n                        \n                        \u003cbr\u003e\n                                            \n                    \u003c\/li\u003e\n                                        \n                    \u003cli\u003e\n                                                Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high\n                        \n                        \u003cbr\u003e\n                                            \n                    \u003c\/li\u003e\n                                    \n                \u003c\/ul\u003e\n                                \n                \u003cb\u003e\n                                        \n                    \u003cbr\u003e\n                                    \n                \u003c\/b\u003e\n                                \n                \u003cb\u003eWho This Book Is For\u003c\/b\u003e\n                                \n                \u003cbr\u003e\n                                \n                \u003cbr\u003e\n                                Anyone interested in discovering and exploiting relationships among variables.  Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.\n            \n            \u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9781484233146\"\u003e\u003ch3\u003eData Patterns and Algorithms for Modern Applications\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9781484233146","offer_id":39566969012317,"sku":"9781484233146","price":80.24,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/0d32a931-8bba-4866-89aa-5c5e4a57d7d1.jpg?v=1775189192","url":"https:\/\/shop.autorenwelt.de\/products\/data-mining-algorithms-in-c-von-timothy-masters","provider":"Autorenwelt Shop","version":"1.0","type":"link"}