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Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish-Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naive Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster-Shafer theory. An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference. Web ResourceThe book's website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the fundamental background required for analyzing data and building models for many practical applications, such as consumer behavior modeling, risk and marketing analytics, and other areas. It also discusses a variety of practical topics that are frequently missing from similar texts. The book begins with the statistical and linear algebra/matrix foundation of modeling methods, from distributions to cumulant and copula functions to Cornish-Fisher expansion and other useful but hard-to-find statistical techniques. It then describes common and unusual linear methods as well as popular nonlinear modeling approaches, including additive models, trees, support vector machine, fuzzy systems, clustering, naive Bayes, and neural nets. The authors go on to cover methodologies used in time series and forecasting, such as ARIMA, GARCH, and survival analysis. They also present a range of optimization techniques and explore several special topics, such as Dempster-Shafer theory. An in-depth collection of the most important fundamental material on predictive analytics, this self-contained book provides the necessary information for understanding various techniques for exploratory data analysis and modeling. It explains the algorithmic details behind each technique (including underlying assumptions and mathematical formulations) and shows how to prepare and encode data, select variables, use model goodness measures, normalize odds, and perform reject inference. Web ResourceThe book's website at www.DataMinerXL.com offers the DataMinerXL software for building predictive models. The site also includes more examples and information on modeling.
Fruits are botanically diverse, perishable, seasonal and predominantly regional in production. They come in many varieties, shapes and size, colors, flavors and textures and are an important part of a healthy diet and the global economy. Besides vitamins, minerals, fibers and other nutrients, fruits contain phenolic compounds that have pharmacological potential. Consumed as a part of a regular diet, these naturally occurring plant constituents are believed to provide a wide range of physiological benefits through their antioxidant, anti-allergic, anti-carcinogenic, and anti-inflammatory properties.  Handbook of Fruits and Fruit Processing distils the latest developments and research efforts in this field that are aimed at improving production methods, post-harvest storage and processing, safety, quality and developing new processes and products. This revised and updated second edition expands and improves upon the coverage of the original book. Some highlights include chapters on the physiology and classification of fruits, horticultural biochemistry, microbiology and food safety (including HACCP, safety and the regulation of fruits in the global market), sensory and flavor characteristics, nutrition, naturally present bioactive phenolics, postharvest physiology, storage, transportation and packaging, processing and preservation technologies. Information on the major fruits includes tropical and super fruits, frozen fruits, canned fruit, jelly, jam and preserves, fruit juices, dried fruits and wines. The 35 chapters are organized into five parts: Part I: Fruit physiology, biochemistry, microbiology, nutrition and health Part II: Postharvest handling and preservation of fruits Part III: Product manufacturing and packaging Part IV: Processing plant, waste management, safety and regulations Part V: Production, quality and processing aspects of major fruits and fruit products Each chapter has been contributed by professionals from around the globe representing academia, government institutions and industry. The book is designed to be a valuable source and reference book for scientists, product developers, students and all professionals with an interest in this field.
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