Search Results - (((((((want OR mantis) OR when) OR partei) OR cantor) OR anne) OR when) OR carter) algorithms.

Artificial neural systems : principle and practice by Lorrentz, Pierre

Table of Contents: “…INTRODUCTIONDENSITY BASED ALGORITHMS: CLUSTERING ALGORITHMS; NATURE-BASED ALGORITHMS; Evolutionary Algorithm and Programming ; Genetic Algorithm; GA Operators; APPLICATIONS OF GENETIC ALGORITHM; NETWORK METHOD: EDGES AND NODES; MULTI-LAYERED PERCEPTRON; REAL-TIME APPLICATIONS OF STATE-OF-THE-ART ANN SYSTEMS; DEFINITION OF ARTIFICIAL NEURAL NETWORKS (ANN); Intelligence; An Artificial Neural Network (ANN) system; PERFORMANCE MEASURES; Receiver's Operating Characteristics (ROC); Hypothesis Testing; Chi-squared (Goodness-of-fit) Test; CONFLICT OF INTEREST; ACKNOWLEDGEMENTS; REFERENCES…”
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Enterprise AI for Dummies. by Jarvinen, Zachary

Table of Contents: “…External partnerships -- The importance of executive buy-in -- Weighing Your Options: Build versus Buy -- When you should do it yourself -- When you should partner with a provider -- Hosting in the Cloud versus On Premises -- What the cloud providers say -- What the hardware vendors say -- The truth in the middle -- Part 2 Exploring Vertical Market Applications -- Chapter 5 Healthcare/HMOs: Streamlining Operations -- Surfing the Data Tsunami -- Breaking the Iron Triangle with Data -- Matching Algorithms to Benefits -- Examining the Use Cases -- Delivering lab documents electronically…”
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Energy Psychology : Explorations at the Interface of Energy, Cognition, Behavior, and Health. by Gallo, Fred P.

Table of Contents: “…Manual Muscle Testing and KinesiologyApplied Kinesiology Offshoots; Empirical Research on Manual Muscle Testing; Muscle Testing Proficiency; Integrity and Muscle Testing; Therapy and Diagnosis; Self-Testing; Therapeutic Algorithms vs. Causal Diagnostics; Energy Psychology and Manual Muscle Testing; Abuses of Manual Muscle Testing and Algorithms; Manual Muscle Testing and Intuition; Unwarranted Uses; When We're Stumped; Systemic Manual Muscle Testing.…”
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Solutions in lidar profiling of the atmosphere by Kovalev, Vladimir A.

Table of Contents: “…1.6.1 Algorithm and Solution Uncertainty1.6.2 Numerical Simulations and Experimental Data; 1.7 Examination of the Remaining Offset in the Backscatter Signal by~Analyzing the Shape of the Integrated Signal; 1.8 Issues in the Examination of the Lidar Overlap Function; 1.8.1 Influence of Distortions in the Lidar Signal when Determining the~Overlap Function; 1.8.2 Issues of Lidar Signal Inversion within the Incomplete Overlap Area; Chapter 2 Essentials and Issues in Separating the Backscatter and Transmission Terms in The Lidar Equation.…”
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Rationality problem for Algebraic Tori by Hoshi, Akinari, 1978-, Yamasaki, Aiichi, 1969-

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Essential US for trauma : E-FAST

Table of Contents: “…Foreword -- Preface -- 1 Basic physics, instrumentation and knobology -- 2 E-FAST protocol: Introduction and focused questions -- 3 Abdominal views: Anatomy, Techniques, Scanning tips and tricks, abnormal images -- 4Thoracic views: Anatomy, Techniques, Scanning Tips and Tricks, Abnormal Images -- 5Including EFAST in Trauma Algorithm: When? What to do next? -- 6 The role of EFAST in Comprehensive US Trauma Management (ABCDE-US) Facing Clinical Scenarios -- 7 Pre-Hospital US in Trauma: Role and Tips -- 8CEUS-FAST: What is it?…”
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Function Estimates. by Marron, J. S.

Table of Contents: “…Contents -- Preface -- Logspline density estimation -- Statistical encounters with B-splines -- Estimation of a transfer function in a nongaussian context -- Evaluating the performance of an inversion algorithm -- Harmonic splines in geomagnetism -- Problems in estimating the anomalous gravity potential of the earth from discrete data -- What regression model should be chosen when the statistician misspecifies the error distribution? …”
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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Can markets compute equilibria? by Monroe, Hunter K.

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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…When to Overload Functions --…”
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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Applied data analytics : principles and applications by Agbinya, Johnson I.

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Applications of digital signal processing to audio and acoustics

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Modeling Reality : How Computers Mirror Life. by Białynicki-Birula, Iwo

Table of Contents: “…Contents; 1 From building blocks to computers: Models and modeling; 2 The game of life: A legendary cellular automaton; 3 Heads or tails: Probability of an event; 4 Galton's board: Probability and statistics; 5 Twenty questions: Probability and information; 6 Snowflakes: The evolution of dynamical systems; 7 The Lorenz butterfly: Deterministic chaos; 8 From Cantor to Mandelbrot: Self-similarity and fractals; 9 Typing monkeys: Statistical linguistics; 10 The bridges of Königsberg: Graph theory; 11 Prisoner's dilemma: Game theory; 12 Let the best man win: Genetic algorithms.…”
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Machine Learning for Mobile : Practical Guide to Building Intelligent Mobile Applications Powered by Machine Learning. by Gopalakrishnan, Revathi

Table of Contents: “…Chapter 2: Supervised and Unsupervised Learning AlgorithmsIntroduction to supervised learning algorithms; Deep dive into supervised learning algorithms; Naive Bayes; Decision trees; Linear regression; Logistic regression; Support vector machines; Random forest; Introduction to unsupervised learning algorithms; Deep dive into unsupervised learning algorithms; Clustering algorithms; Clustering methods; Hierarchical agglomerative clustering methods; K-means clustering; Association rule learning algorithm; Summary; References; Chapter 3: Random Forest on iOS; Introduction to algorithms…”
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Python unlocked : become more fluent in Python--learn strategies and techniques for smart and high-performance Python programming by Tigeraniya, Arun

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Explorations in Computing : an Introduction to Computer Science. by Conery, John S.

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Prognostics and Health Management of Engineering Systems : an Introduction. by Kim, Nam-Ho

Table of Contents: “…2.2.2 When a Degradation Model Is Available (Physics-Based Approaches)2.2.2.1 Problem Definition; 2.2.2.2 Parameter Estimation and Degradation Prediction; 2.2.2.3 Effect of Noise in Data; 2.2.3 When a Degradation Model Is NOT Available (Data-Driven Approaches); 2.2.3.1 Function Evaluation; 2.2.3.2 Overfitting; 2.2.3.3 Prognosis with More Training Data; 2.3 RUL Prediction; 2.3.1 RUL; 2.3.2 Prognostics Metrics; 2.3.2.1 Prognostic Horizon (PH); 2.3.2.2 varvec{ alpha { -- } lambda} Accuracy; 2.3.2.3 (Cumulative) Relative Accuracy (RA, CRA); 2.3.2.4 Convergence; 2.3.2.5 Results with MATLAB Code.…”
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Reliability in biomechanics by Kharmanda, Ghias, El Hami, Abdelkhalak

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Data Science : The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field

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