Search Results - (((((((ant OR alter) OR find) OR kkantor) OR cantor) OR anne) OR salted) OR wanting) algorithms.

Disorders of the Patellofemoral Joint. by Fulkerson, John P.

Table of Contents: “…SOFT-TISSUE DYSPLASIANonarthritic Anterior Knee Pain; CLINICAL FEATURES; Symptoms; Physical Findings; RETINACULAR PAIN; PLICA OR SYNOVITIS; PATELLAR TENDINITIS (JUMPER'S KNEE); Prepatellar Bursitis; Retropatellar Tendon Bursitis; Pes Anserinus Bursitis; Fat Pad Syndrome; MENISCAL LESIONS; CRUCIATE LIGAMENT DEFICIENCY AND RECONSTRUCTION; HEMANGIOMA; RUNNERS' KNEE; ILIOTIBIAL FRICTION BAND SYNDROME; REFERRED PAIN; REHABILITATION OF SOFT-TISSUE PROBLEMS; Patellar Tilt Compression and the Excessive Lateral Pressure Syndrome; CLINICAL FEATURES; Signs and Symptoms.…”
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Node.js web development by Herron, David

Table of Contents: “…Node.js's algorithm for require (module)Module identifiers and path names; An example application directory structure; npm -- the Node.js package management system; The npm package format; Finding npm packages; Other npm commands; Installing an npm package; Initializing a new npm package; Maintaining package dependencies with npm; Fixing bugs by updating package dependencies; Declaring Node.js version compatibility; Updating outdated packages you've installed; Installing packages from outside the npm repository; Publishing an npm package; Package version numbers; A quick note about CommonJS.…”
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Tales of Literacy for the 21st Century : the Literary Agenda. by Wolf, Maryanne

Table of Contents: “…; Deep reading and what comes next; A first algorithm for what comes next; Notes; 7: A Tale of Hope for Non-Literate Children.…”
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Introduction to Machine Learning with Python. by Chopra, Deepti

Table of Contents: “…Machine Learning Involves A Complex Process -- Insufficient training data -- Feasibility of Learning An Unknown Target Function -- Collection of Data -- Pre-processing of Data -- Finding The Model That Will Be Best For The Data -- Training and Testing Of The Developed Model Evaluation -- In Sample Error and Out of Sample Error -- APPLICATIONS OF MACHINE LEARNING -- Virtual Personal Assistants -- Traffic Prediction -- Online Transportation Networks -- Video Surveillance System -- Social Media Services -- People you May Know -- Face Recognition -- Similar Pins -- Sentiment Analysis…”
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The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; 8 Building the Nautiluscoin Economy; Dynamic Proof-of-Stake; Nautiluscoin Gross Domestic Product Target; Algorithmic Monetary Policy.…”
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Ruby Under a Microscope : Learning Ruby Internals Through Experiment. by Shaughnessy, Pat

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Adaptive tests of significance using permutations of residuals with R and SAS by O'Gorman, Thomas W.

Table of Contents: “…2.3.1 An Estimator of Variability Based on Traditional Percentiles2.3.2 R Code for Finding the Bandwidth; 2.3.3 An Estimator of Variability Based on Percentiles from the Smoothed Distribution Function; 2.4 Normalizing Transformations; 2.4.1 Traditional Normalizing Methods; 2.4.2 Normalizing Data by Weighting; 2.5 The Weighting Algorithm; 2.5.1 An Example of the Weighing Procedure; 2.5.2 R Code for Weighting the Observations; 2.6 Computing the Bandwidth; 2.6.1 Error Distributions; 2.6.2 Measuring Errors in Adaptive Weighting; 2.6.3 Simulation Studies; 2.7 Examples of Transformed Data.…”
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Pediatric incontinence : evaluation and clinical management

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Numerical Methods : Using MATLAB. by Lindfield, G. R. (George R.)

Table of Contents: “…6.1 Classification of Second-Order Partial Differential Equations -- 6.2 The Shooting Method -- 6.3 The Finite Difference Method -- 6.4 Two-Point Boundary Value Problems -- 6.5 Parabolic Partial Differential Equations -- 6.6 Hyperbolic Partial Differential Equations -- 6.7 Elliptic Partial Differential Equations -- 6.8 Summary -- Problems -- 7 Fitting Functions to Data -- 7.1 Introduction -- 7.2 Interpolation Using Polynomials -- 7.3 Interpolation Using Splines -- 7.4 Fourier Analysis of Discrete Data -- 7.5 Multiple Regression: Least Squares Criterion -- 7.6 Diagnostics for Model Improvement -- 7.7 Analysis of Residuals -- 7.8 Polynomial Regression -- 7.9 Fitting General Functions to Data -- 7.10 Nonlinear Least Squares Regression -- 7.11 Transforming Data -- 7.12 Summary -- Problems -- 8 Optimization Methods -- 8.1 Introduction -- 8.2 Linear Programming Problems -- 8.3 Optimizing Single-Variable Functions -- 8.4 The Conjugate Gradient Method -- 8.5 Moller's Scaled Conjugate Gradient Method -- 8.6 Conjugate Gradient Method for Solving Linear Systems -- 8.7 Genetic Algorithms -- 8.8 Continuous Genetic Algorithm -- 8.9 Simulated Annealing -- 8.10 Constrained Nonlinear Optimization -- 8.11 The Sequential Unconstrained Minimization Technique -- 8.12 Summary -- Problems -- 9 Applications of the Symbolic Toolbox -- 9.1 Introduction to the Symbolic Toolbox -- 9.2 Symbolic Variables and Expressions -- 9.3 Variable-Precision Arithmetic in Symbolic Calculations -- 9.4 Series Expansion and Summation -- 9.5 Manipulation of Symbolic Matrices -- 9.6 Symbolic Methods for the Solution of Equations -- 9.7 Special Functions -- 9.8 Symbolic Differentiation -- 9.9 Symbolic Partial Differentiation -- 9.10 Symbolic Integration -- 9.11 Symbolic Solution of Ordinary Differential Equations -- 9.12 The Laplace Transform -- 9.13 The Z-Transform.…”
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Knowledge mining using intelligent agents

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Digital wealth : an automatic way to invest successfully by Moore, Simon, 1978-

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Meeting security challenges through data analytics and decision support

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Introduction to Stochastic Processes with R. by Dobrow, Robert P.

Table of Contents: “…2.6 MATHEMATICAL INDUCTION* -- CHAPTER 3: MARKOV CHAINS FOR THE LONG TERM -- 3.1 LIMITING DISTRIBUTION -- 3.2 STATIONARY DISTRIBUTION -- 3.3 CAN YOU FIND THE WAY TO STATE a? -- 3.4 IRREDUCIBLE MARKOV CHAINS -- 3.5 PERIODICITY -- 3.6 ERGODIC MARKOV CHAINS -- 3.7 TIME REVERSIBILITY -- 3.8 ABSORBING CHAINS -- 3.9 REGENERATION AND THE STRONG MARKOV PROPERTY* -- 3.10 PROOFS OF LIMIT THEOREMS* -- CHAPTER 4: BRANCHING PROCESSES -- 4.1 INTRODUCTION -- 4.2 MEAN GENERATION SIZE -- 4.3 PROBABILITY GENERATING FUNCTIONS -- 4.4 EXTINCTION IS FOREVER -- CHAPTER 5: MARKOV CHAIN MONTE CARLO -- 5.1 INTRODUCTION…”
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Generalizations of Cyclostationary Signal Processing : Spectral Analysis and Applications. by Napolitano, Antonio

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How to measure anything in cybersecurity risk by Hubbard, Douglas W., 1962-, Seiersen, Richard, 1967-

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Fundamentals of patenting and licensing for scientists and engineers by Ma, Matthew Y.

Table of Contents: “…Anatomy of a patent. 4.2. Find your easiest entry point -- a first glance. 4.3. …”
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Optimization and anti-optimization of structures under uncertainty by Elishakoff, Isaac

Table of Contents: “…Why don't the probabilistic methods find appreciation among theoreticians and practitioners alike? …”
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Oral cancer surgery : a visual guide by Kesting, Marco

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Layout techniques for integrated circuit designers by Sahrling, Mikael, 1964-

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Kaplan's clinical hypertension by Kaplan, Norman M., 1931-2020, Victor, Ronald G., Flynn, Joseph T.

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