Search Results - (((((((ant OR wind) OR hate) OR alter) OR cantor) OR anne) OR find) OR wanting) algorithms.

  1. 201
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    Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

    Published 2012
    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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  3. 203

    Deterministic global optimization : an introduction to the diagonal approach by Sergeyev, Yaroslav D., 1963-, Kvasov, Dmitri E.

    Published 2017
    Table of Contents: “…3.1 General Diagonal Scheme3.2 Analysis of Traditional Diagonal Partition Schemes; 3.3 Non-redundant Diagonal Partition Strategy; 4 Global Optimization Algorithms Based on the Non-redundant Partitions ; 4.1 Multiple Estimates of the Lipschitz Constant; 4.2 Derivative-Free Diagonal Method MultL; 4.2.1 Theoretical Background of MultL: Lower Bounds; 4.2.2 Theoretical Background of MultL: Finding Non-dominated Hyperintervals; 4.2.3 Description of the MultL Algorithm and its Convergence Analysis; 4.3 One-Point-Based Method MultK for Differentiable Problems.…”
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  4. 204

    Hierarchical Protection for Smart Grids. by Ma, Jing

    Published 2018
    Table of Contents: “…3.3 Local Area Protection for Centralized Renewable Energy3.3.1 Connection Form of a Wind Farm and its Protection Configuration; 3.3.2 Adaptive Distance Protection Scheme for Wind Farm Collector Lines; 3.3.3 Differential Protection Scheme for Wind Farm Outgoing Transmission Line; 3.4 Local Area Protection for Distributed Renewable Energy; 3.4.1 Adaptive Protection Approach for a Distribution Network Containing Distributed Generation; 3.4.2 Islanding Detection Method; 3.5 Summary; References; Chapter 4 Topology Analysis; 4.1 Introduction; 4.2 Topology Analysis for the Inner Substation.…”
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  5. 205

    Machine learning with R quick start guide : a beginner's guide to implementing machine learning techniques from scratch using R 3.5 by Pastor Sanz, Iván

    Published 2019
    Table of Contents: “…Accessing components and elements in a listData frames; Accessing elements in data frames; Functions of data frames; Importing or exporting data; Working with functions; Controlling code flow; All about R packages; Installing packages; Necessary packages; Taking further steps; Background on the financial crisis; Summary; Chapter 2: Predicting Failures of Banks -- Data Collection; Collecting financial data; Why FDIC?; Listing files; Finding files; Combining results; Removing tables; Knowing your observations; Handling duplications; Operating our problem; Collecting the target variable…”
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  6. 206

    Computer Mathematics : Proceedings of the Fifth Asian Symposium (Ascm 2001). by Shirayanagi, Kiyoshi

    Published 2001
    Table of Contents: “…Preface ; Invited Talks ; Finding All the Solutions of Cyclic 9 Using Grobner Basis Techniques ; On the Complexity of Computing Determinants (Extended Abstract) ; Papers.…”
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  7. 207

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

    Published 1999
    Table of Contents: “…Discovering patterns in DNA sequences by the algorithmic significance method / Aleksandar Milosavljevic -- Assembling blocks / Jorja G. …”
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  8. 208

    Focus on artificial neural networks

    Published 2011
    Table of Contents: “…ARTIFICIAL NEURAL NETWORKS (ANNS) -- 3. MICROEMULSIONS -- 4. APPLICATION OF ANNS IN THE DEVELOPMENT OF MICROEMULSION DRUG DELIVERY SYSTEMS -- 4.1. …”
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  9. 209

    Monte Carlo methods and applications : proceedings of the 8th IMACS Seminar on Monte Carlo Methods, August 29-September 2, 2011, Borovets, Bulgaria

    Published 2013
    Table of Contents: “…9.3 General Description of the Coupled Atmosphere Fire Modeling and WRF-Fire9.4 Wind Simulation Approach; 9.5 Conclusion; 10 Wireless Sensor Network Layout; 10.1 Introduction; 10.2 Wireless Sensor Network Layout Problem; 10.3 ACO for WSN Layout Problem; 10.4 Experimental Results; 10.5 Conclusion; 11 A Two-Dimensional Lorentzian Distribution for an Atomic Force Microscopy Simulator; 11.1 Introduction; 11.2 Modeling Oxidation Kinetics; 11.3 Development of the Lorentzian Model; 11.3.1 Algorithm for the Gaussian Model; 11.3.2 Development of the Lorentzian Model; 11.4 Conclusion.…”
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    Electronic Conference Proceeding eBook
  10. 210

    Pattern Recognition in Computational Molecular Biology : Techniques and Approaches by Elloumi, Mourad, Iliopoulos, Costas, Wang, Jason T. L., Zomaya, Albert Y.

    Published 2015
    Table of Contents: “…2.4 Techniques for Proving Inapproximability Results for String Barcoding Problems2.5 Heuristic Algorithms for String Barcoding Problems; 2.6 Conclusion; Acknowledgments; References; Chapter 3: Alignment-Free Measures for Whole-Genome Comparison; 3.1 Introduction; 3.2 Whole-Genome Sequence Analysis; 3.3 Underlying Approach; 3.4 Experimental Results; 3.5 Conclusion; Author's Contributions; 3.6 Acknowledgments; References; Chapter 4: A Maximum Likelihood Framework for Multiple Sequence Local Alignment; 4.1 Introduction; 4.2 Multiple Sequence Local Alignment; 4.3 Motif Finding Algorithms.…”
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  11. 211

    Machine Learning with Core ML : an IOS Developer's Guide to Implementing Machine Learning in Mobile Apps. by Newnham, Joshua

    Published 2018
    Table of Contents: “…Learning algorithms Auto insurance in Sweden; Supported learning algorithms; Considerations ; Summary; Chapter 3: Recognizing Objects in the World; Understanding images; Recognizing objects in the world; Capturing data ; Preprocessing the data; Performing inference ; Summary ; Chapter 4: Emotion Detection with CNNs; Facial expressions; Input data and preprocessing ; Bringing it all together; Summary ; Chapter 5: Locating Objects in the World; Object localization and object detection ; Converting Keras Tiny YOLO to Core ML; Making it easier to find photos; Optimizing with batches; Summary.…”
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  12. 212

    Metaheuristics for vehicle routing problems. by Labadie, Nacima

    Published 2015
    Table of Contents: “…Memetic algorithm; 4.1.3. Memetic algorithm with population management; 4.1.4. …”
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  13. 213

    Industrial control systems

    Published 2011
    Table of Contents: “…EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION ; 3. PLANT MODEL BASED ON ANN ; 3.1. ANN Configurations.…”
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    Placement preparation : more than 1000 questions by Parthasarathy, S.

    Published 2006
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  17. 217

    Comprehensive Ruby Programming. by Hudgens, Jordan

    Published 2017
    Table of Contents: “…Implementing a prime number counting algorithm -- How to code the power digit sum algorithm -- Implementing a humanize counting algorithm -- Implementing a date algorithm -- How to code a Fibonacci digit counter -- Implementing a permutation algorithm -- Implementing an amicable number algorithm -- Implementing a factorial algorithm -- Implementing an even Fibonacci number algorithm -- Implementing the least common multiple -- Summary -- Chapter 19: Machine Learning -- Big data analysis -- Basic decision tree implementation -- Addition of attributes -- Addition of training data and its values -- Calling the ID3 method -- Function of the train method -- Addition of the test method -- Setting up decision variables -- Print the results out -- Testing the program -- Advantage of decision trees over if ... else statements -- How to develop a big data analysis application in Ruby -- Summary -- Index.…”
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  18. 218

    MATLAB for Machine Learning. by Ciaburro, Giuseppe

    Published 2017
    Table of Contents: “…Multiple linear regression with categorical predictorPolynomial regression; Regression Learner App; Summary; Chapter 5: Pattern Recognition through Classification Algorithms; Predicting a response by decision trees; Probabilistic classification algorithms -- Naive Bayes; Basic concepts of probability; Classifying with Naive Bayes; Bayesian methodologies in MATLAB; Describing differences by discriminant analysis; Find similarities using nearest neighbor classifiers; Classification Learner app; Summary; Chapter 6: Identifying Groups of Data Using Clustering Methods; Introduction to clustering.…”
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  19. 219

    Applications of swarm intelligence

    Published 2011
    Table of Contents: “…Algorithm for Ms3 ""; ""4.4. Ms4: Combination of Ms1 and Ms2 ""; ""4.5. …”
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