Search Results - (((((((ant OR wantii) OR antine) OR find) OR cantor) OR anne) OR shape) OR hints) algorithms.

Ant Colony Optimization and Constraint Programming. by Solnon, Christine

Table of Contents: “…Constraint programming; 1.1.2. Ant colony optimization; 1.1.3. Constraint programming with ant colony optimization; Chapter 2. …”
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Algorithms of education : how datafication and artificial intelligence shape policy / by Gulson, Kalervo N., Sellar, Sam, Webb, P. Taylor

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Theory of randomized search heuristics : foundations and recent developments

Table of Contents: “…Runtime Analysis of Evolutionary Algorithms for Discrete Optimization Peter S. Oliveto and Xin Yao; 3. …”
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Algorithmic Puzzles. by Levitin, Anany, Levitin, Maria

Table of Contents: “…-- General strategies for algorithm design -- Analysis techniques -- Easier puzzles (#1 to #50) -- Puzzles of medium difficulty (#51 to #110) -- Harder puzzles (#111 to #150) -- Hints -- Solutions -- References -- Design stragety and analysis index.…”
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The rise of the algorithms : how YouTube and TikTok conquered the world by Jordan, John M.

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Algorithms in Bioinformatics : a Practical Introduction by Sung, Wing-Kin

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Convergence theorems for lattice group-valued measures. by Antonio Boccuto; Xenofon Dimitriou

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Algorithms for Dummies. by Mueller, John Paul

Table of Contents: “…Intro -- Title Page -- Copyright Page -- Table of Contents -- Introduction -- About This Book -- Foolish Assumptions -- Icons Used in This Book -- Beyond the Book -- Where to Go from Here -- Part 1 Getting Started with Algorithms -- Chapter 1 Introducing Algorithms -- Describing Algorithms -- The right way to make toast: Defining algorithm uses -- Finding algorithms everywhere -- Using Computers to Solve Problems -- Getting the most out of modern CPUs and GPUs -- Working with special-purpose chips -- Networks: Sharing is more than caring -- Leveraging available data…”
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The Human Algorithm by Solis, Brian

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Algorithmic composition : a guide to composing music with Nyquist by Simoni, Mary Hope

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Nature-inspired optimization algorithms by Yang, Xin-She

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Permutation Group Algorithms by Seress, kos

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Multi-Objective Optimization in Theory and Practice II : metaheuristic algorithms. by Keller, André A.

Table of Contents: “…Near Pareto-Optimal Front; 1.3.4. Shapes of a Pareto-Optimal Front; 1.4. Selection Procedures of Algorithms; 1.4.1. …”
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Algorithms in invariant theory by Sturmfels, Bernd, 1962-

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Finding Alphas : a Quantitative Approach to Building Trading Strategies. by Tulchinsky, Igor

Table of Contents: “…Data Inputs to an AlphaAlpha Universe; Alpha Prediction Frequency; Value of an Alpha; Practical Alpha Evaluation; Future Performance; Conclusion; 5 How to Develop an Alpha: A Case Study; Conclusion; 6 Data and Alpha Design; How we find Data for Alphas; Data Validation; Understand the Data Before Using it; Embrace the Big Data ERA; Conclusion; 7 Turnover; Alpha Horizon; The Cost of a Trade; The Crossing Effect; Controlling Turnover; Examples; Tuning the Turnover; 8 Alpha Correlation; Alpha PnL Correlation; Alpha Value Correlation; Correlation with Alpha Pool; Conclusion…”
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Swarm intelligence and bio-inspired computation : theory and applications

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Ant colony optimization by Dorigo, Marco

Table of Contents: “…Preface; Acknowledgments; 1 -- From Real to Artificial Ants; 2 -- The Ant Colony Optimization Metaheuristic; 3 -- Ant Colony Optimization Algorithms for the Traveling Salesman Problem; 4 -- Ant Colony Optimization Theory; 5 -- Ant Colony Optimization for NP-Hard Problems; 6 -- AntNet: An ACO Algorithm for Data Network Routing; 7 -- Conclusions and Prospects for the Future; Appendix: Sources of Information about the ACO Field; References; Index.…”
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Stabilisation and Motion Control of Unstable Objects by Formalʹskiĭ, A. M. (Aleksandr Moiseevich)

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The golden ticket : P, NP, and the search for the impossible by Fortnow, Lance, 1963-

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Mastering Machine Learning Algorithms : Expert techniques to implement popular machine learning algorithms and fine-tune your models. by Bonaccorso c/o Quandoo, Giuseppe

Table of Contents: “…Conditional probabilities and Bayes' theoremBayesian networks; Sampling from a Bayesian network; Direct sampling; Example of direct sampling; A gentle introduction to Markov chains; Gibbs sampling; Metropolis-Hastings sampling; Example of Metropolis-Hastings sampling; Sampling example using PyMC3; Hidden Markov Models (HMMs); Forward-backward algorithm; Forward phase; Backward phase; HMM parameter estimation; Example of HMM training with hmmlearn; Viterbi algorithm; Finding the most likely hidden state sequence with hmmlearn; Summary; Chapter 5: EM Algorithm and Applications.…”
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