Search Results - (((((((ken OR want) OR frantin) OR markant) OR cantor) OR anne) OR shared) OR hints) algorithms.

Data analysis with R : load, wrangle, and analyze your data using the world's most powerful statistical programming language by Fischetti, Tony

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Atlas of minimally invasive surgery for lung and esophageal cancer

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Choice modelling : the state-of-the-art and the state-of-practice : proceedings from the inaugural International Choice Modelling Conference

Table of Contents: “…On estimation of hybrid choice models / Denis Bolduc, Ricardo Alvarez-Daziano -- A model of travel happiness and mode switching / Maya Abou-Zeid, Moshe Ben-Akiva -- On path generation algorithms for route choice models / Emma Frejinger, Michel Bierlaire -- Mode choice endogeneity in value of travel time estimation / Stefan L. …”
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Model Building in Mathematical Programming. by Williams, H. Paul

Table of Contents: “…Intro -- Title Page -- Copyright -- Dedication -- Preface -- Preface to the Fifth Edition -- Part I -- Chapter 1: Introduction -- 1.1 The concept of a model -- 1.2 Mathematical programming models -- Chapter 2: Solving mathematical programming models -- 2.1 Algorithms and packages -- 2.2 Practical considerations -- 2.3 Decision support and expert systems -- 2.4 Constraint programming (CP) -- Chapter 3: Building linear programming models -- 3.1 The importance of linearity -- 3.2 Defining objectives -- 3.3 Defining constraints -- 3.4 How to build a good model…”
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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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New Horizons in Mobile and Wireless Communications, Volume 1 : Radio Interfaces. by Prasad, Ramjee

Table of Contents: “…1.3 Research and Standardization Activities Toward New Radio Interfaces1.3.1 European-Funded Research Activities; 1.3.2 Other Activities; 1.4 Preview of the Book; References; Chapter 2 Spectrum-Efficient Radio Interface Technologies; 2.1 Introduction; 2.1.1 Radio Interfaces for Ubiquitous Communications; 2.1.2 OFDM-Based Radio Interfaces in the Scope of Next Generation Systems; 2.1.3 Coexistence and Spectrum Sharing; 2.1.4 Opportunities for Secondary Spectrum Use; 2.1.5 Multiband Transmissions; 2.2 Radio Interfaces Optimized for PANs; 2.2.1 Scenarios and Radio Propagation Models for PANs.…”
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Uncommon sense : investment wisdom since the stock market's dawn by Kemp, Michael E.

Table of Contents: “…-- RELATING BOOK VALUE PER SHARE TO MARKET PRICE -- WHAT IS A LOW P/B RATIO TELLING US? …”
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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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Biomedical image analysis recipes in MATLAB : for life scientists and engineers by Reyes-Aldasoro, Constantino Carlos

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Intelligent IoT for the Digital World : Incorporating 5G Communications and Fog/Edge Computing Technologies. by Yang, Yang

Table of Contents: “…1.3.1 Data Collection Technologies -- 1.3.1.1 mmWave -- 1.3.1.2 Massive MIMO -- 1.3.1.3 Software Defined Networks -- 1.3.1.4 Network Slicing -- 1.3.1.5 Time Sensitive Network -- 1.3.1.6 Multi-user Access Control -- 1.3.1.7 Muti-hop Routing Protocol -- 1.3.2 Computing Power Network -- 1.3.2.1 Intelligent IoT Computing Architecture -- 1.3.2.2 Edge and Fog Computing -- 1.3.3 Intelligent Algorithms -- 1.3.3.1 Big Data -- 1.3.3.2 Artificial Intelligence -- 1.4 Typical Applications -- 1.4.1 Environmental Monitoring -- 1.4.2 Public Safety Surveillance -- 1.4.3 Military Communication…”
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Advanced reliability modeling : proceedings of the 2004 Asian International Workshop (AIWARM 2004) : Hiroshima, Japan, 26-27 August 2004

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The spirit of the hive : the mechanisms of social evolution by Page, Robert E.

Table of Contents: “…The regulatory architecture of pollen hoarding -- 9.1. Loading algorithms -- 9.2. Heritability of the pollen-hoarding syndrome -- 9.3. …”
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Stochastic optimization models in finance

Table of Contents: “…The Main Theorem and an Algorithm; V. Nonterminating Processes; ACKNOWLEDGMENT; REFERENCES; CHAPTER5. …”
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A cabinet of philosophical curiosities : a collection of puzzles, oddities, riddles, and dilemmas by Sorensen, Roy A.

Table of Contents: “…; Descartes's Disappearance; The Most Fairly Distributed Good; Fairness Framed; Towards a Fairer Share of Dishwashing; What the Dishwasher Missed; Developmental Self-Defeat; Random Quiz; Enforcing Gresham's Law; Gresham's Law of Numbers; Laziest Reductio.…”
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Adventures in Authentic Learning : 21 Step-by-Step Projects From an Edtech Coach. by Harrington, Kristin

Table of Contents: “…Lesson Plans -- Lesson Plan 2.1 PechaKucha Presentations -- Lesson Plan 2.2 Life Cycles Jigsaw Research Project -- Lesson Plan 2.3 Math Jigsaw Project -- Lesson Plan 2.4 Student-Created Tutorial Videos -- Coach's Connection -- CHAPTER 3: Collaborate for Success -- Some Lesser-Known Educator Sharing Tools -- Enhance Projects with Content Experts -- Provide an Authentic Audience -- Engaging Students in Peer Review -- Lesson Plans -- Lesson Plan 3.1 Algorithmic Thinking Project -- Lesson Plan 3.2 Pick Your Path Stories -- Lesson Plan 3.3 Invent It Challenge…”
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Pediatric incontinence : evaluation and clinical management

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Corporate strategy for dramatic productivity surge

Table of Contents: “…Instantaneous and spontaneous information sharing / Hiromichi Yasuoka -- ch. 7. The development of a three-minute battery charger / Toru Fujii -- ch. 8. …”
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Accelerating MATLAB with GPU computing : a primer with examples by Suh, Jung W., Kim, Youngmin

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Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

Table of Contents: “…DISCOVERING KNOWLEDGE IN DATA -- Contents -- Preface -- 1 An Introduction to Data Mining -- 1.1 What is Data Mining? -- 1.2 Wanted: Data Miners -- 1.3 The Need for Human Direction of Data Mining -- 1.4 The Cross-Industry Standard Practice for Data Mining -- 1.4.1 Crisp-DM: The Six Phases -- 1.5 Fallacies of Data Mining -- 1.6 What Tasks Can Data Mining Accomplish? …”
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Clinical simulation : operations, engineering and management

Table of Contents: “…; 3.5 The Systems Approach to Training; 3.6 Defining the Performance Requirement; 3.7 Cost Versus Value Added; 3.8 Operations Cost; 3.9 Standardization: What is it, and who Wants it?; 3.10 Patients as Training Conditions; 3.11 Equipment as Training Conditions; 3.12 Increase in Training System Cost; 3.13 You as the Leader-Manager; 3.14 Conclusion; Endnotes; Topic II What's In It For Me.…”
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