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Summary: "Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals."--

Summary: This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R & D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R & D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book's companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner's reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book's R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge.

The problem of estimating the intensity process of a doubly stochastic Poisson process is analyzed. Using covariance information, a recursive linear minimum mean-square error estimate is designed. Moreover, an efficient procedure for the computation of its associated error covariance is shown. The proposed solution becomes an alternative approach to the Kalman filter which is applicable under the only structural assumption that the intensity process to be estimated has a finite-dimensional covariance function. [ABSTRACT FROM AUTHOR]

This paper presents the thermodynamic equilibrium predictions for steam reforming of vegetable oil in a thermal plasma reformer. An optimal operating condition has been chosen at temperature of 800°C and mole flow ratio of vegetable oil to water of 1:18 using software simulation. The predicted results show that mole fraction of hydrogen reaches, respectively, 61% and 63% on wet and dry bases for proton exchange membrane fuel cell applications. In addition, the total mole fractions of fuels for solid oxide fuel cell applications approaches 95% and even over 98% on wet and dry bases, respectively. These predicted data will be compared with experimental data conducted with a 1kWe thermal plasma reformer at Clean Energy R&D Center, Da-Yeh University in Taiwan, R.O.C. [ABSTRACT FROM AUTHOR]

Practical aspects of Kalman filtering applications are considered in this chapter, as follows: [ABSTRACT FROM PUBLISHER]

The book is organized for use as a text for an introductory course in stochastic processes at the senior level and as a first-year graduate-level course in Kalman filtering theory and application. Chapter 1 provides an informal introduction to the general subject matter by way of its history of development and application. [ABSTRACT FROM PUBLISHER]

This chapter presents a unified probabilistic and algorithmic framework for nonlinear dual estimation methods. Also included is a brief review of the EKF for both state and weight estimation, and an introduction of some of the complications in coupling the two. An example in noisy time‐series prediction is provided. The development of a general probabilistic framework for dual estimation is detailed which allows the relating of various methods that have been presented in literature, and also provides a general algorithmic approach leading to a number of different dual EKF algorithms. Results on additional example data sets are also presented. [ABSTRACT FROM PUBLISHER]

The celebrated Kalman filter, rooted in the state-space formulation of linear dynamical systems, provides a recursive solution to the linear optimal filtering problem. It applies to stationary as well as nonstationary environments. The solution is recursive in that each updated estimate of the state is computed from the previous estimate and the new input data, so only the previous estimate requires storage. In addition to eliminating the need for storing the entire past observed data, the Kalman filter is computationally more efficient than computing the estimate directly from the entire past observed data at each step of the filtering process.

The article presents information on forecasting theory. The ability to forecast future events accurately has been highly valued throughout time. Forecasting methods can be classified into two groups, qualitative and quantitative methods. Qualitative or judgmental forecasting methods are subjective in nature. They are based on intuition, personal knowledge, and experience and are educated guesses of forecasters or experts in the field. These forecasts can be generated very informally or follow a structured decision-making process.

The article presents information on filtering theory. Filtering theory is an interesting and diverse field of study with many practical applications in a variety of areas. From the early developments due to the pioneers A.M. Legrendre and C.F. Gauss through modern-day developments due to G. Campbell, K. Wagner, N. Wiener, A. Kolmogorov, and R. Kalman filtering theory has played an important role in engineering, science, manufacturing, and economics. Simply stated, a filter is a system that operates on an input signal in such a way as to produce a desired output signal. Many familiar processes, separating noise from signals, smoothing and prediction, integrating and differentiating signals, are examples of filters.