Search Results - (((((((ant OR alter) OR find) OR iskantor) OR cantor) OR anne) OR salted) OR wanting) algorithms.

A Primer on Machine Learning Applications in Civil Engineering by Deka, Paresh Chandra

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- Acknowledgments -- A Primer on Machine Learning Applications in Civil Engineering -- Author -- 1: Introduction -- 1.1 Machine Learning -- 1.2 Learning from Data -- 1.3 Research in Machine Learning: Recent Progress -- 1.4 Artificial Neural Networks -- 1.5 Fuzzy Logic (FL) -- 1.6 Genetic Algorithms -- 1.7 Support Vector Machine (SVM) -- 1.8 Hybrid Approach (HA) -- Bibliography -- 2: Artificial Neural Networks -- 2.1 Introduction to Fundamental Concepts and Terminologies -- 2.2 Evolution of Neural Networks -- 2.3 Models of ANN -- 2.4 McCulloch-Pitts Model -- 2.5 Hebb Network -- 2.6 Summary -- 2.7 Supervised Learning Network -- 2.7.1 Perceptron Network -- 2.7.2 Adaptive Linear Neuron -- 2.7.3 Back-Propagation Network -- 2.7.4 Radial Basis Function Network -- 2.7.5 Generalized Regression Neural Networks -- 2.7.6 Summary -- 2.8 Unsupervised Learning Networks -- 2.8.1 Introduction -- 2.8.2 Kohonen Self-Organizing Feature Maps -- 2.8.3 Counter Propagation Network -- 2.8.4 Adaptive Resonance Theory Network -- 2.8.5 Summary -- 2.9 Special Networks -- 2.9.1 Introduction -- 2.9.2 Gaussian Machine -- 2.9.3 Cauchy Machine -- 2.9.4 Probabilistic Neural Network -- 2.9.5 Cascade Correlation Neural Network -- 2.9.6 Cognitive Network -- 2.9.7 Cellular Neural Network -- 2.9.8 Optical Neural Network -- 2.9.9 Summary -- 2.10 Working Principle of ANN -- 2.10.1 Introduction -- 2.10.2 Types of Activation Function -- 2.10.3 ANN Architecture -- 2.10.4 Learning Process -- 2.10.5 Feed-Forward Back Propagation -- 2.10.6 Strengths of ANN -- 2.10.7 Weaknesses of ANN -- 2.10.8 Working of the Network -- 2.10.9 Summary -- Bibliography -- 3: Fuzzy Logic -- 3.1 Introduction to Classical Sets and Fuzzy Sets -- 3.1.1 Classical Sets -- 3.1.2 Fuzzy Sets -- 3.1.3 Summary.…”
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Blockchain-based peer-to-peer transactions in energy systems by Nwulu, Nnamdi, Damisa, Uyikumhe

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The linguistic cerebellum

Table of Contents: “…VOICE RECORDING MATERIALVOICE RECORDING SESSION; ANALYSIS ALGORITHM AND EFFECTS; SPINOCEREBELLAR ATAXIA DIAGNOSIS USING SPEECH ANALYSIS; ACOUSTIC FINDINGS IN CEREBELLAR PATIENTS; CONCLUSION; REFERENCES; 8 -- Cerebellum and Writing; INTRODUCTION; WRITING; AGRAPHIA; CEREBELLUM; DISCUSSION; CONCLUSION; REFERENCES; 9 -- The Role of the Cerebellum in Developmental Dyslexia; INTRODUCTION; IS THE CEREBELLUM PART OF THE READING NETWORK?…”
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Digital Learning in Motion : From Book Culture to the Digital Age. by Kergel, David

Table of Contents: “…4.3 The Urban Counterpublic of the Urban Avant-Garde: From Dadaism to Street Art -- 4.4 Progressive Education as (Counter- )Pedagogy of Modern Urbanism -- 4.5 Fordism -- the Economic Structure of Modern Urbanity -- 4.6 Informal-Accidental Learning Via Unidirectional Mass Media -- 4.7 In the Television Era the Electronic Age Finds its Medial Climax -- 4.8 Reacting on Informal-Accidental Learning -- Media Literacy, Media Competence and Media-Bildung -- 5 Fluid Learning in the Digital Age -- 5.1 The Beginning of the Digital Age -- 5.2 Early Counterculture of the 1990s Net Utopists.…”
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Lung cancer and imaging

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Mobile opportunistic networks : architectures, protocols and applications

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Advanced practice nursing guide to the neurological exam by Armitage, Alex

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Complexus mundi : emergent patterns in nature

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Artificial Intelligence in Wireless Communications. by Rondeau, Thomas W.

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Microwave and millimeter wave circuits and systems : emerging design, technologies, and applications

Table of Contents: “…1.1.7 MBF Model -- the Memoryless PA Behavioural Model of ChoiceAcknowledgements; References; 2 Artificial Neural Network in Microwave Cavity Filter Tuning; 2.1 Introduction; 2.2 Artificial Neural Networks Filter Tuning; 2.2.1 The Inverse Model of the Filter; 2.2.2 Sequential Method; 2.2.3 Parallel Method; 2.2.4 Discussion on the ANN's Input Data; 2.3 Practical Implementation -- Tuning Experiments; 2.3.1 Sequential Method; 2.3.2 Parallel Method; 2.4 Influence of the Filter Characteristic Domain on Algorithm Efficiency; 2.5 Robots in the Microwave Filter Tuning; 2.6 Conclusions; Acknowledgement…”
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Java Data Mining - Strategy, Standard, and Practice : a Practical Guide for architecture, design, and implementation. by Hornick, Mark F.

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Learning Python Design Patterns - Second Edition. by Giridhar, Chetan

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Process control design for industrial applications by Popescu, Dumitru (Writer on computer science and engineering), Gharbi, Amira, Stefanoiu, Dan, Borne, Pierre

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Towards Cognitive Autonomous Networks : Network Management Automation for 5G and Beyond. by Mwanje, Stephen S.

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Radio Propagation in the Urban Scenario. by Franceshetti, Giorgio

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Lead generation for dummies by Rothman, Dayna

Table of Contents: “…Defining Your SEO Measurements and AnalyticsPart III: Linking Outbound Marketing with Lead Generation; Chapter 11: Helping Buyers Find You with Pay-Per-Click Ads; Getting Started; Creating Your Ad Copy; Tracking Your Ad Performance; Chapter 12: Casting a Wide Net with Content Syndication; Accomplishing Your Goals with Content Syndication; Implementing Non-Paid Content Syndication; Chapter 13: Targeting with a Personal Touch Through Direct Mail; Making Direct Mail Work for You; Focusing on Creative Execution; Having an Integrated Approach.…”
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A guide to Monte Carlo simulations in statistical physics by Landau, David P.

Table of Contents: “…3.7 Finding the groundstate of a Hamiltonian -- 3.8 Generation of 'random' walks -- 3.8.1 Introduction -- 3.8.2 Random walks -- 3.8.3 Self-avoiding walks -- 3.8.4 Growing walks and other models -- 3.9 Final remarks -- References -- 4 Importance sampling Monte Carlo methods -- 4.1 Introduction -- 4.2 The simplest case: single spin-flip sampling for the simple Ising model -- 4.2.1 Algorithm -- 4.2.2 Boundary conditions -- 4.2.3 Finite size effects -- 4.2.4 Finite sampling time effects -- 4.2.5 Critical relaxation -- 4.3 Other discrete variable models.…”
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Advanced numerical and semi analytical methods for differential equations by Chakraverty, Snehashish

Table of Contents: “…8.2.2.1 Heaviside Function8.2.2.2 Dirac Delta Function; 8.2.2.3 Finding the Fundamental Solution; 8.2.3 Green's Function; 8.2.3.1 Green's Integral Formula; 8.3 Derivation of the Boundary Element Method; 8.3.1 BEM Algorithm; References; Chapter 9 Akbari-Ganji's Method; 9.1 Introduction; 9.2 Nonlinear Ordinary Differential Equations; 9.2.1 Preliminaries; 9.2.2 AGM Approach; 9.3 Numerical Examples; 9.3.1 Unforced Nonlinear Differential Equations; 9.3.2 Forced Nonlinear Differential Equation; References; Chapter 10 Exp-Function Method; 10.1 Introduction; 10.2 Basics of Exp-Function Method…”
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Trading on sentiment : the power of minds over markets by Peterson, Richard L., 1972-

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Machine learning in non-stationary environments : introduction to covariate shift adaptation by Sugiyama, Masashi, 1974-

Table of Contents: “…5.2 Characterization of Hetero-Distributional Subspace5.3 Identifying Hetero-Distributional Subspace by Supervised Dimensionality Reduction; 5.4 Using LFDA for Finding Hetero-Distributional Subspace; 5.5 Density-Ratio Estimation in the Hetero-Distributional Subspace; 5.6 Numerical Examples; 5.7 Summary; 6 Relation to Sample Selection Bias; 6.1 Heckman's Sample Selection Model; 6.2 Distributional Change and Sample Selection Bias; 6.3 The Two-Step Algorithm; 6.4 Relation to Covariate Shift Approach; 7 Applications of Covariate Shift Adaptation; 7.1 Brain-Computer Interface.…”
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