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Bronson with Rebecca Bronson 210 $aLanham $cRowman & Littlefield$dc2013 215 $a1 online resource (193 p.) 300 $aDescription based upon print version of record. 311 $a1-4422-2101-1 311 $a1-299-73417-0 320 $aIncludes bibliographical references and index. 327 $aIn defense of estrogen -- The truth about progesterone -- Mood chemistry -- The connection between body type and hormones -- Weaving the web : how hormones are central to the female psyche -- Sexuality -- Relationships -- Diseases of aging/adventures in aging. 330 $aHormone Replacement Therapy is vital for women, and there is a lot of confusion about it in both the medical community and among women. This book presents the science behind bio-identical hormones and describes why these hormones are so important for women, especially at midlife. 606 $aHormones 606 $aWomen$xHealth and hygiene 608 $aElectronic books. 615 0$aHormones. 615 0$aWomen$xHealth and hygiene. 676 $a612.4/05 700 $aBronson$b Phyllis J.$f1952-$0966121 701 $aBronson$b Rebecca$0966122 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910452269403321 996 $aMoods, emotions, and aging$92192446 997 $aUNINA LEADER 05632nam 22007333u 450 001 9910132334903321 005 20240404170240.0 010 $a9781118691786 010 $a1118691784 035 $a(CKB)3710000000111791 035 $a(EBL)1687540 035 $a(FR-PaCSA)88944254 035 $a(MiAaPQ)EBC1687540 035 $a(FRCYB88944254)88944254 035 $a(EXLCZ)993710000000111791 100 $a20140519d2014|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig data, data mining, and machine learning $evalue creation for business leaders and practitioners 205 $a1st ed. 210 1$aHoboken :$cWiley,$d2014 215 $a1 online resource (289 pages) 225 1 $aWiley and SAS business series 225 1 $aTHEi Wiley ebooks 300 $aDescription based upon print version of record. 311 08$a9781118618042 311 08$a1118618041 327 $aBig Data, Data Mining, and Machine Learning; Contents; Forward; Preface; Acknowledgments; Introduction; Big Data Timeline; Why This Topic Is Relevant Now; Is Big Data a Fad?; Where Using Big Data Makes a Big Difference; Technical Issue; Work Flow Productivity; The Complexities When Data Gets Large; Part One The Computing Environment; Chapter 1 Hardware; Storage (Disk); Central Processing Unit; Graphical Processing Unit; Memory; Network; Chapter 2 Distributed Systems; Database Computing; File System Computing; Considerations; Chapter 3 Analytical Tools; Weka; Java and JVM Languages; R; Python 327 $aSASPart Two Turning Data into Business Value; Chapter 4 Predictive Modeling; A Methodology for Building Models; sEMMA; sEMMA for the Big Data Era; Binary Classification; Multilevel Classification; Interval Prediction; Assessment of Predictive Models; Classification; Receiver Operating Characteristic; Lift; Gain; Akaike's Information Criterion; Bayesian Information Criterion; Kolmogorov‐Smirnov; Chapter 5 Common Predictive Modeling Techniques; RFM; Regression; Basic Example of Ordinary Least Squares; Assumptions of Regression Models; Additional Regression Techniques 327 $aApplications in the Big Data EraGeneralized Linear Models; Example of a Probit GLM; Applications in the Big Data Era; Neural Networks; Basic Example of Neural Networks; Decision and Regression Trees; Support Vector Machines; Bayesian Methods Network Classification; Naive Bayes Network; Parameter Learning; Learning a Bayesian Network; Inference in Bayesian Networks; Scoring for Supervised Learning; Ensemble Methods; Chapter 6 Segmentation; Cluster Analysis; Distance Measures (Metrics); Evaluating Clustering; Number of Clusters; K-means Algorithm; Hierarchical Clustering; Profiling Clusters 327 $aChapter 7 Incremental Response ModelingBuilding the Response Model; Measuring the Incremental Response; Chapter 8 Time Series Data Mining; Reducing Dimensionality; Detecting Patterns; Fraud Detection; New Product Forecasting; Time Series Data Mining in Action: Nike+ FuelBand; Seasonal Analysis; Trend Analysis; Similarity Analysis; Chapter 9 Recommendation Systems; What Are Recommendation Systems?; Where Are They Used?; How Do They Work?; Baseline Model; Low‐Rank Matrix Factorization; Stochastic Gradient Descent; Alternating Least Squares; Restricted Boltzmann Machines; Contrastive Divergence 327 $aAssessing Recommendation QualityRecommendations in Action: SAS Library; Chapter 10 Text Analytics; Information Retrieval; Content Categorization; Text Mining; Text Analytics in Action: Let's Play Jeopardy!; Information Retrieval Steps; Discovering Topics in Jeopardy! Clues; Topics from Clues Having Incorrect or Missing Answers; Discovering New Topics from Clues; Contestant Analysis: Fantasy Jeopardy!; Part Three Success Stories of Putting It All Together; Chapter 11 Case Study of a Large U.S.-Based Financial Services Company; Traditional Marketing Campaign Process 327 $aHigh-Performance Marketing Solution 330 $aWith big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. 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