Search Results - (((((((kant OR anti) OR wants) OR mantis) OR cantor) OR anne) OR file) OR splitting) algorithms.

Data Clustering : Algorithms and Applications. by Aggarwal, Charu C.

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Theory of randomized search heuristics : foundations and recent developments

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The alphabet and the algorithm by Carpo, Mario

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Algorithmic trading : winning strategies and their rationale by Chan, Ernest P., 1966-

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

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Algorithms and complexity by Wilf, Herbert S., 1931-2012

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Bioinformatics algorithms : techniques and applications

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Derivatives algorithms. Volume 1, Bones by Hyer, Tom

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Automatic sequences by Haeseler, Friedrich von

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Nature-inspired optimization algorithms by Yang, Xin-She

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Fundamental Data Compression. by Pu, Ida Mengyi

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Jewels of stringology : text algorithms. by RYTTER, WOJCIECH

Table of Contents: “…Parallel string-matching algorithms. 16.3. * Splitting technique. 16.4. Parallel KMR algorithm and application. 16.5. …”
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Algorithms in invariant theory by Sturmfels, Bernd, 1962-

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Algorithms for worst-case design and applications to risk management by Rustem, Berc

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Stabilisation and Motion Control of Unstable Objects by Formalʹskiĭ, A. M. (Aleksandr Moiseevich)

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Pathway modeling and algorithm research

Table of Contents: “…PATHWAY MODELING AND ALGORITHM RESEARCH; PATHWAY MODELING AND ALGORITHM RESEARCH; Contents; Preface; Biological Pathways and Their Modeling; Abstract; 1. …”
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Distributed control of robotic networks : a mathematical approach to motion coordination algorithms by Bullo, Francesco, Cortés, Jorge, 1974-, Martínez, Sonia, 1974-

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The mathematica guidebook for numerics by Trott, Michael, Ph. D.

Subjects: “…Mathematica (Computer file)…”
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Data Mining Algorithms : Explained Using R. by Cichosz, Pawel

Table of Contents: “…3.3.1 Algorithm outline3.3.2 Class distribution calculation; 3.3.3 Class label assignment; 3.3.4 Stop criteria; 3.3.5 Split selection; 3.3.6 Split application; 3.3.7 Complete process; 3.4 Pruning; 3.4.1 Pruning operators; 3.4.2 Pruning criterion; 3.4.3 Pruning control strategy; 3.4.4 Conversion to rule sets; 3.5 Prediction; 3.5.1 Class label prediction; 3.5.2 Class probability prediction; 3.6 Weighted instances; 3.7 Missing value handling; 3.7.1 Fractional instances; 3.7.2 Surrogate splits; 3.8 Conclusion; 3.9 Further readings; References; Chapter 4 Naïve Bayes classifier; 4.1 Introduction.…”
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Learning with kernels : support vector machines, regularization, optimization, and beyond by Schölkopf, Bernhard

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