Timothy Masters was a lonely, troubled teenager with a penchant for gory artwork when he first saw Peggy Lee Hettrick……her dead, mutilated body nearly frozen in the early morning of Fort Collins, Colorado. Not believing it could really be a dead body, thinking he was the victim of yet another prank by his abusive classmates, the fifteen-year-old didn’t go to the police—but they came to him. So began a decade-long investigation led by a relentless detective who was sure that Masters was the killer, even without a shred of physical evidence. Against all reason, a conspiracy of silence and circumstantial evidence eventually put Masters behind bars. Only the determination of a lone investigator who believed the young man was innocent would reveal the shocking truth, and free Masters after ten years in prison. This is the compelling true story of one life ended in blood and murder, one life ruined by coincidence and prejudice, and justice long denied but finally found.

Drawn to Injustice: The Wrongful Conviction of Timothy Masters

Biographies & Memoirs

Lehto, Steve

Masters, Timothy

True Crime

BERKLEY

Demonstrates how neural networks can be used to aid in the solution of digital signal processing (DSP) or imaging problems. A large section is devoted to the design and training of complex-domain multiple-layer feedforward networks (MLFNs)—all essential equations are presented and justified. Reviews the most popular signal- and image-processing algorithms, emphasizing those that are particularly suitable for union to complex-domain neural networks. Features a wide variety of problems for which complex-domain networks significantly outperform their real-domain counterparts. The accompanying disk includes complete source code for algorithms discussed with full source for program examples.

Signal and Image Processing with Neural Networks: A C++ Sourcebook

Computers & Technology

Certification

wiley

A valuable working resource for anyone who uses neural networks to solve real-world problems 
This practical guide contains a wide variety of state-of-the-art algorithms that are useful in the design and implementation of neural networks. All algorithms are presented on both an intuitive and a theoretical level, with complete source code provided on an accompanying disk. Several training algorithms for multiple-layer feedforward networks (MLFN) are featured. The probabilistic neural network is extended to allow separate sigmas for each variable, and even separate sigma vectors for each class. The generalized regression neural network is similarly extended, and a fast second-order training algorithm for all of these models is provided. The book also discusses the recently developed Gram-Charlier neural network and provides important information on its strengths and weaknesses. Readers are shown several proven methods for reducing the dimensionality of the input data. Advanced Algorithms for Neural Networks also covers: <ul>
<li>Advanced multiple-sigma PNN and GRNN training, including conjugate-gradient optimization based on cross validation</li> <li>The Levenberg-Marquardt training algorithm for multiple-layer feedforward networks</li> <li>Advanced stochastic optimization, including Cauchy simulated annealing and stochastic smoothing</li> <li>Data reduction and orthogonalization via principal components and discriminant functions</li> <li>Economical yet powerful validation techniques, including the jackknife, the bootstrap, and cross validation</li> <li>Includes a complete state-of-the-art PNN/GRNN program, with both source and executable code</li> </ul>

Advanced Algorithms for Neural Networks: A C++ Sourcebook

An authoritative guide to predicting the future using neural, novel, and hybrid algorithms Expert Timothy Masters provides you with carefully paced, step-by-step advice and guidance plus the proven tools and techniques you need to develop successful applications for business forecasting, stock market prediction, engineering process control, economic cycle tracking, marketing analysis, and more. Neural, Novel &amp; Hybrid Algorithms for Time Series Prediction provides information on: * Robust confidence intervals for predictions made with neural, ARIMA, and other models * Wavelets for detecting features that presage important events * Multivariate ARMA models for simultaneous prediction of multiple series based on multiple inputs and shocks * Hybrid ARMA/neural models to improve the accuracy of predictions * Data reduction and orthogonalization using principal components and related operations * Digital filters for preprocessing to enhance useful information and suppress noise * Diagnostic tools such as the maximum entropy spectrum and Savitzky-Golay filters for suggesting and validating prediction models * Effective preprocessing techniques for prediction with neural networks CD-ROM INCLUDES: * PREDICT-both DOS and Windows NT versions-a powerful time series program that can be easily customized to make accurate predictions in any application area * Much useful source code, including the complex-general multivariate fast Fourier transform in both C++ and Pentium-optimized assembler

Neural, Novel & Hybrid Algorithms for Time Series Prediction

Computer Science

John Wiley&Sons

This book begins by presenting methods for performing practical, real-life assessment of the performance of prediction and classification models. It then goes on to discuss techniques for improving the performance of such models by intelligent resampling of training/testing data, combining multiple models into sophisticated committees, and making use of exogenous information to dynamically choose modeling methodologies. Rigorous statistical techniques for computing confidence in predictions and decisions receive extensive treatment. Finally, a hundred pages are devoted to the use of information theory in evaluating and selecting useful predictors. Special attention is paid to Schreiber's Information Transfer, a recent generalization of Grainger Causality. Well commented C++ code is given for every algorithm and technique. The ultimate purpose of this text is three-fold. The first goal is to open the eyes of serious developers to some of the hidden pitfalls that lurk in the model development process. The second is to provide broad exposure for some of the most powerful model enhancement algorithms that have emerged from academia in the last two decades, while not bogging down readers in cryptic mathematical theory. Finally, this text should provide the reader with a toolbox of ready-to-use C++ code that can be easily incorporated into his or her existing programs.

Assessing and Improving Prediction and Classification

Science & Math

Mathematics

CreateSpace Independent Publishing Platform

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. 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. What You'll Learn
<ul>
<li>Use Monte-Carlo permutation tests to provide statistically sound assessments of relationships present in your data 
</li>
<li>Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data 
</li>
<li>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 
</li>
<li>See how the eigenstructure of a dataset enables clustering of variables into groups that exist only within meaningful subspaces of the data 
</li>
<li>Plot regions of the variable space where there is disagreement between marginal and actual densities, or where contribution to mutual information is high 
</li>
</ul>
 Who This Book Is For 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.

Data Mining Algorithms in C++: Data Patterns and Algorithms for Modern Applications

Apress

Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application. Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics. All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Many of these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program. What You'll Learn
<ul>
<li>Compute entropy to detect problematic predictors 
</li>
<li>Improve numeric predictions using constrained and unconstrained combinations, variance-weighted interpolation, and kernel-regression smoothing 
</li>
<li>Carry out classification decisions using Borda counts, MinMax and MaxMin rules, union and intersection rules, logistic regression, selection by local accuracy, maximization of the fuzzy integral, and pairwise coupling 
</li>
<li>Harness information-theoretic techniques to rapidly screen large numbers of candidate predictors, identifying those that are especially promising 
</li>
<li>Use Monte-Carlo permutation methods to assess the role of good luck in performance results 
</li>
<li>Compute confidence and tolerance intervals for predictions, as well as confidence levels for classification decisions 
</li>
</ul>
 Who This Book is For Anyone who creates prediction or classification models will find a wealth of useful algorithms in this book. Although all code examples are written in C++, the algorithms are described in sufficient detail that they can easily be programmed in any language.

Assessing and Improving Prediction and Classification: Theory and Algorithms in C++

Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn
<ul>
<li>Employ deep learning using C++ and CUDA C 
</li>
<li>Work with supervised feedforward networks 
</li>
<li>Implement restricted Boltzmann machines 
</li>
<li>Use generative samplings 
</li>
<li>Discover why these are important 
</li>
</ul>
 Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Deep Belief Nets in C++ and CUDA C: Volume 1: Restricted Boltzmann Machines and Supervised Feedforward Networks

Business & Money

Industries

Discover the essential building blocks of a common and powerful form of deep belief net: the autoencoder. You’ll take this topic beyond current usage by extending it to the complex domain for signal and image processing applications. Deep Belief Nets in C++ and CUDA C: Volume 2 also covers several algorithms for preprocessing time series and image data. These algorithms focus on the creation of complex-domain predictors that are suitable for input to a complex-domain autoencoder. Finally, you’ll learn a method for embedding class information in the input layer of a restricted Boltzmann machine. This facilitates generative display of samples from individual classes rather than the entire data distribution. The ability to see the features that the model has learned for each class separately can be invaluable. At each step this book provides you with intuitive motivation, a summary of the most important equations relevant to the topic, and highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. What You'll LearnCode for deep learning, neural networks, and AI using C++ and CUDA CCarry out signal preprocessing using simple transformations, Fourier transforms, Morlet wavelets, and moreUse the Fourier Transform for image preprocessingImplement autoencoding via activation in the complex domainWork with algorithms for CUDA gradient computationUse the DEEP operating manualWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Deep Belief Nets in C++ and CUDA C: Volume 2: Autoencoding in the Complex Domain

Discover the essential building blocks of a common and powerful form of deep belief network: convolutional nets. This book shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a ‘thought process’ that is capable of learning abstract concepts built from simpler primitives. These models are especially useful for image processing applications. At each step Deep Belief Nets in C++ and CUDA C: Volume 3 presents intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. Source code for all routines presented in the book, and the executable CONVNET program which implements these algorithms, are available for free download.What You Will LearnDiscover convolutional nets and how to use themBuild deep feedforward nets using locally connected layers, pooling layers, and softmax outputsMaster the various programming algorithms requiredCarry out multi-threaded gradient computations and memory allocations for this threadingWork with CUDA code implementations of all core computations, including layer activations and gradient calculationsMake use of the CONVNET program and manual to explore convolutional nets and case studiesWho This Book Is ForThose who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.

Deep Belief Nets in C++ and CUDA C: Volume 3: Convolutional Nets

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