Search Results - (((((((ant OR want) OR mantic) OR win) OR cantor) OR anne) OR shape) OR hints) algorithms.

Modern optimization methods for science, engineering and technology

Table of Contents: “…Implementing the traveling salesman problem using a modified ant colony optimization algorithm -- 5.1. ACO and candidate list -- 5.2. …”
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Rough fuzzy image analysis : foundations and methodologies

Table of Contents: “…1. Cantor, fuzzy, near, and rough sets in image analysis / James F. …”
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Digital holography by Picart, Pascal

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Uniqueness questions in reconstruction of multidimensional objects from tomography-type projection data by Golubyatnikov, V. P. (Vladimir P.)

Table of Contents: “…Stability estimates for recovering the shapes of convex bodies from the shapes of their projections -- Chapter 3. …”
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Stochastic Models in Reliability Engineering. by Cui, Lirong

Table of Contents: “…5.3 Time-Varying Reliability Evaluation Based on MDD -- 5.3.1 The Construction of System MDD -- 5.3.2 System Reliability Evaluation Based on MDD -- 5.4 Numerical Studies -- 5.4.1 Illustrative Example -- 5.4.2 System MDD for the Illustrative Example -- 5.4.3 System Reliability Assessment for the Illustrative Example -- 5.5 Conclusions -- References -- Chapter 6 An Upside-Down Bathtub-Shaped Failure Rate Model Using a DUS Transformation of Lomax Distribution -- 6.1 Introduction -- 6.2 DUS-Lomax Distribution -- 6.3 Shapes -- 6.3.1 Shape of Probability Density Function…”
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Differential algebra and related topics - proceedings of the international workshop. by SIT, WILLIAM Y.

Table of Contents: “…6 Reduction Algorithms 7 Rosenfeld Properties of an Autoreduced Set ; 8 Coherence and Rosenfeld's Lemma ; 9 Ritt-Raudenbush Basis Theorem ; 10 Decomposition Problems ; 11 Component Theorems ; 12 The Low Power Theorem ; Appendix: Solutions and hints to selected exercises ; References…”
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Fast sequential Monte Carlo methods for counting and optimization by Rubinstein, Reuven Y.

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Multi-agent machine learning : a reinforcement approach

Table of Contents: “…2.12 Eligibility TracesReferences; Chapter 3: Learning in Two-Player Matrix Games; 3.1 Matrix Games; 3.2 Nash Equilibria in Two-Player Matrix Games; 3.3 Linear Programming in Two-Player Zero-Sum Matrix Games; 3.4 The Learning Algorithms; 3.5 Gradient Ascent Algorithm; 3.6 WoLF-IGA Algorithm; 3.7 Policy Hill Climbing (PHC); 3.8 WoLF-PHC Algorithm; 3.9 Decentralized Learning in Matrix Games; 3.10 Learning Automata; 3.11 Linear Reward-Inaction Algorithm; 3.12 Linear Reward-Penalty Algorithm; 3.13 The Lagging Anchor Algorithm; 3.14 L R-I Lagging Anchor Algorithm; References.…”
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Radon Transforms and Tomography. by Quinto, Eric Todd

Table of Contents: “…Expository Papers -- Local tomography and related problems -- Tomography problems arising in synthetic aperture radar -- Introduction to local tomography -- Algorithms in ultrasound tomography -- Radon transforms, differential equations, and microlocal analysis -- Supplementary bibliography to A bibliographic survey of the Pompeiu problem -- II. …”
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Pervasive Computing in Cognitive Communications Applications. by Ghinea, George

Table of Contents: “…Cover -- Guest editorial -- Single-loop sigma delta modulator design and verification for cognitive IoT applications -- Efficient task scheduling on virtual machine in cloud computing environment -- High gain switched beam Yagi-Uda antenna for millimeter wave communications -- PAPR reduction using OQAM for spectrum sensing -- Energy efficiency in cloud data centres using parallel hybrid Jaya algorithm -- Soft computing based audio signal analysis for accident prediction -- Kuznets curve with parabola shaped ultra-wideband antenna with defected ground plane for communications…”
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Analysis of biological data : a soft computing approach

Table of Contents: “…Feature selection for cancer classification using ant colony optimization and support vector machines / A. …”
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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications by Lantz, Brett

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Smart intelligent computing and communication technology

Table of Contents: “…Intro -- Title Page -- Preface -- Committees -- Contents -- Evolution of Computing Intelligence -- A Novel Compression Algorithm Based on Bio-Orthogonal Transform and Encoding Methods for Computed Tomography Images -- Performance Analysis of K-Nearest Neighbor Classification Algorithms for Bank Loan Sectors -- An Effective Comparative Analysis of Data Preprocessing Techniques in Network Intrusion Detection System Using Deep Neural Networks -- Identification of Sickle Cells in Erythrocytes Images Using Shape Descriptors -- Impact of Personality Traits on Interpersonal Dependency…”
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Reconstructing evolution : new mathematical and computational advances

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Rediscovering mathematics : you do the math by Simonson, Shai

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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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Numerical methods for viscosity solutions and applications

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DSP with Python Programming. by Charbit, Maurice

Table of Contents: “…Gaussian linear case: Kalman algorithm; 3.5. Discrete finite Markov case; 4 Monte-Carlo Methods; 4.1. …”
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Artificial intelligence with Python : build real-world artificial intelligence applications with Python to intelligently interact with the world around you by Joshi, Prateek

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MATLAB for Machine Learning. by Ciaburro, Giuseppe

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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