Search Results - (((((((want OR hands) OR wanting) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Security of self-organizing networks : MANET, WSN, WMN, VANET

Table of Contents: “…Other Online and Adaptive Algorithms --…”
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When life is linear : from computer graphics to bracketology by Chartier, Timothy P.

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Life sciences, information sciences

Table of Contents: “…Some proteins share the same evolutionary history --…”
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Handbook of performability engineering

Table of Contents: “…Tampered Failure Rate Load-Sharing Systems: Status and Perspectives /…”
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Learning Boost C++ libraries : solve practical programming problems using powerful, portable, and expressive libraries from Boost by Mukherjee, Arindam

Table of Contents: “…Shared ownership semanticsboost::shared_ptr and std::shared_ptr; Intrusive smart pointers -- boost::intrusive_ptr; shared_array; Managing non-memory resources using smart pointers; Self-test questions; Summary; References; Chapter 4: Working with Strings; Text processing with Boost String Algorithms library; Using Boost String Algorithms; Find algorithms; Case-conversion and trimming algorithms; The replace and erase algorithms; The split and join algorithms; Splitting text using the Boost Tokenizer library; Tokenizing based on separators…”
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Media technologies : essays on communication, materiality, and society

Table of Contents: “…Bowker -- "What Do We Want?" "Materiality!" "When Do We Want It?" "Now!" …”
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Open innovation : new product development essentials from the PDMA

Table of Contents: “…Pricing Algorithm --…”
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Advanced Analytics with R and Tableau. by Stirrup, Jen

Table of Contents: “…; How to do Clustering in Tableau; Creating Clusters; Clustering example in Tableau; Creating a Tableau group from cluster results; Constraints on saving Clusters; Interpreting your results; How Clustering Works in Tableau; The clustering algorithm; Scaling; Clustering without using k-means; Hierarchical modeling; Statistics for Clustering; Describing Clusters -- Summary tab; Testing your Clustering.…”
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F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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Pattern discovery in biomolecular data : tools, techniques, and applications

Table of Contents: “…Discovering patterns in DNA sequences by the algorithmic significance method / Aleksandar Milosavljevic -- Assembling blocks / Jorja G. …”
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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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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…Algorithms --…”
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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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Methods for Network Optimization and Parallel Derivative-free Optimization. by Olsson, Per-Magnus

Table of Contents: “…Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgements -- Thesis Introduction -- Contents -- Part I Contributions to Network Optimization -- 1 Introduction -- 2 Related Work -- 3 The Relay Positioning Problems -- 4 Environment Representation and Discretization -- 5 Relay Positioning Algorithms for Single Target Problems -- 6 Relay Positioning for Multiple Targets -- 7 Implementation and Experimental Results -- 8 Discussion -- Bibliography -- Part II Contributions to Derivative-free Optimization -- 9 Introduction -- 10 Setting And Problem Description -- 11 Direct Search Algorithms -- 12 Model Building Algorithms -- 13 Existing Model Building Algorithms -- 14 Radial Basis Functions -- 15 Parallelism in Algorithms for Derivative-Free Optimization -- 16 Information Sharing -- 17 Control of Optimization Algorithms -- 18 Implementation Details -- 19 Testing on Synthetic Test Cases -- 20 Testing On Industrial Test Cases -- 21 Future Work -- 22 Conclusions -- A Terms -- Bibliography.…”
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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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Navigation signal processing for GNSS software receivers by Pany, Thomas

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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios, 1951-

Table of Contents: “…Probability and stochastic processes -- Learning in parametric modeling: basic concepts and directions -- Mean-square error linear estimation -- Stochastic gradient descent: the LMS algorithm -- The least-squares family -- Classification: a tour of the classics -- Parameter learning: a convex analytic path -- Sparsity-aware learning: concepts and theoretical foundations -- Sparcity-aware learning: algorithms and applications -- Learning in reproducing Kernel Hilbert spaces -- Bayesian learning: inference and the EM alogrithm -- Bayesian learning: approximate inference and nonparametric models -- Monte Carlo methods -- Probabilistic graphical models: Part I -- Probabilistic graphical models: Part II -- Particle filtering -- Neural networks and deep learning -- Dimensionality reduction -- Appendix A LInear algebra -- Appendix B Probability theory and statistics -- Appendix C Hints on constrained optimization.…”
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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…11.2 Kohonen Networks -- 11.2.1 Kohonen Networks Algorithm -- 11.3 Example of a Kohonen Network Study -- 11.4 Cluster Validity -- 11.5 Application of Clustering Using Kohonen Networks -- 11.6 Interpreting the Clusters -- 11.6.1 Cluster Profiles -- 11.7 Using Cluster Membership as Input to Downstream Data Mining Models -- THE R ZONE -- References -- Exercises -- Hands-On Analysis -- 12 Association Rules -- 12.1 Affinity Analysis and Market Basket Analysis -- 12.1.1 Data Representation for Market Basket Analysis -- 12.2 Support, Confidence, Frequent Itemsets, and the a Priori Property -- 12.3 How Does the a Priori Algorithm Work? …”
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Autonomous mobile robots and multi-robot systems : motion-planning, communication, and swarming

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Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

Table of Contents: “…Regression Analysis -- The Regression Model -- Correlation and Causation -- Algorithms for Regression Analysis -- Assessing Regression Model Performance -- Model Validity -- Looking Beyond R2 -- Polynomial Regression -- Artificial Neural Networks for Regression Analysis -- Dataset Preparation -- Tutorial -- A Regression Model for Home Appraisal -- Modeling with the Right Set of Observations -- Exercise 6.1 -- ANN Modeling -- The Advantage of ANN Regression -- Top-Down Attribute Selection -- Issues in Model Interpretation -- Model Validation -- Model Application -- Summary…”
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