Search Results - (((((((want OR mantis) OR wants) OR alter) OR cantor) OR anne) OR when) OR wanting) algorithms.

Rationality problem for Algebraic Tori by Hoshi, Akinari, 1978-, Yamasaki, Aiichi, 1969-

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Numerical Methods for Eigenvalue Problems. by Börm, Steffen

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Deep Learning with Pytorch Quick Start Guide : Learn to Train and Deploy Neural Network Models in Python. by Julian, David

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Data Science : The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field

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Mastering Scala machine learning by Kozlov, Alexander

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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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Enterprise AI for Dummies. by Jarvinen, Zachary

Table of Contents: “…External partnerships -- The importance of executive buy-in -- Weighing Your Options: Build versus Buy -- When you should do it yourself -- When you should partner with a provider -- Hosting in the Cloud versus On Premises -- What the cloud providers say -- What the hardware vendors say -- The truth in the middle -- Part 2 Exploring Vertical Market Applications -- Chapter 5 Healthcare/HMOs: Streamlining Operations -- Surfing the Data Tsunami -- Breaking the Iron Triangle with Data -- Matching Algorithms to Benefits -- Examining the Use Cases -- Delivering lab documents electronically…”
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Essential US for trauma : E-FAST

Table of Contents: “…Foreword -- Preface -- 1 Basic physics, instrumentation and knobology -- 2 E-FAST protocol: Introduction and focused questions -- 3 Abdominal views: Anatomy, Techniques, Scanning tips and tricks, abnormal images -- 4Thoracic views: Anatomy, Techniques, Scanning Tips and Tricks, Abnormal Images -- 5Including EFAST in Trauma Algorithm: When? What to do next? -- 6 The role of EFAST in Comprehensive US Trauma Management (ABCDE-US) Facing Clinical Scenarios -- 7 Pre-Hospital US in Trauma: Role and Tips -- 8CEUS-FAST: What is it?…”
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Insight into Theoretical and Applied Informatics : Introduction to Information Technologies and Computer Science. by Yatsko, Andrzej

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Evolutionary optimization

Table of Contents: “…Cover -- Contents -- Preface -- Contributing Authors -- Part I Introduction -- 1 Conventional Optimization Techniques -- 1 Classifying Optimization Models -- 2 Linear Programming -- 3 Goal Programming -- 4 Integer Programming -- 5 Nonlinear Programming -- 6 Simulation -- 7 Further Reading -- 2 Evolutionary Computation -- 1 What Is Evolutionary Computation -- 2 A Brief Overview of Evolutionary Computation -- 3 Evolutionary Algorithm and Generate-and-Test Search Algorithm -- 4 Search Operators -- 5 Summary -- Part II Single Objective Optimization -- 3 Evolutionary Algorithms and Constrained Optimization -- 1 Introduction -- 2 General considerations -- 3 Numerical optimization -- 4 Final Remarks -- 4 Constrained Evolutionary Optimization -- 1 Introduction -- 2 The Penalty Function Method -- 3 Stochastic Ranking -- 4 Global Competitive Ranking -- 5 How Penalty Methods Work -- 6 Experimental Study -- 7 Conclusion -- Appendix: Test Function Suite -- Part III Multi-Objective Optimization -- 5 Evolutionary Multiobjective Optimization -- 1 Introduction -- 2 Definitions -- 3 Historical Roots -- 4 A Quick Survey of EMOO Approaches -- 5 Current Research -- 6 Future Research Paths -- 7 Summary -- 6 MEA for Engineering Shape Design -- 1 Introduction -- 2 Multi-Objective Optimization and Pareto-Optimality -- 3 Elitist Non-dominated Sorting GA (NSGA-II) -- 4 Hybrid Approach -- 5 Optimal Shape Design -- 6 Simulation Results -- 7 Conclusion -- 7 Assessment Methodologies for MEAs -- 1 Introduction -- 2 Assessment Methodologies -- 3 Discussion -- 4 Comparing Two Algorithms: An Example -- 5 Conclusions and Future Research Paths -- Part IV Hybrid Algorithms -- 8 Hybrid Genetic Algorithms -- 1 Introduction -- 2 Hybridizing GAs with Local Improvement Procedures -- 3 Adaptive Memory GA's -- 4 Summary -- 9 Combining choices of heuristics -- 1 Introduction -- 2 GAs and parameterised algorithms -- 3 Job Shop Scheduling -- 4 Scheduling chicken catching -- 5 Timetabling -- 6 Discussion and future directions -- 10 Nonlinear Constrained Optimization -- 1 Introduction -- 2 Previous Work -- 3 A General Framework to look for SPdn -- 4 Experimental Results -- 5 Conclusions -- Part V Parameter Selection in EAs -- 11 Parameter Selection -- 1 Introduction -- 2 Parameter tuning vs. parameter control -- 3 An example -- 4 Classification of Control Techniques -- 5 Various forms of control -- 6 Discussion -- Part VI Application of EAs to Practical Problems -- 12 Design of Production Facilities -- 1 Introduction -- 2 Design for Material Flow When the Number of I/O Points is Unconstrained -- 3 Design for Material Flow for a Single I/O Point -- 4 Considering Intradepartmental Flow -- 5 Material Handling System Design -- 6 Concluding Remarks -- 13 Virtual Population and Acceleration Techniques -- 1 Introduction -- 2 Concept of Virtual Population -- 3 Solution Acceleration Techniques -- 4 Accelerated GA and Acceleration Sche.…”
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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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Principles of Quantum Computation and Information, Volume I : Basic Concepts. by Casati, Giulio, Strini, Giuliano

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Function Estimates. by Marron, J. S.

Table of Contents: “…Contents -- Preface -- Logspline density estimation -- Statistical encounters with B-splines -- Estimation of a transfer function in a nongaussian context -- Evaluating the performance of an inversion algorithm -- Harmonic splines in geomagnetism -- Problems in estimating the anomalous gravity potential of the earth from discrete data -- What regression model should be chosen when the statistician misspecifies the error distribution? …”
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Creating E-Learning Games with Unity. by Horachek, David

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The silicon jungle : a novel of deception, power, and Internet intrigue by Baluja, Shumeet, 1971-

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Handbook of biometrics for forensic science

Table of Contents: “…5.2 Background of Fingerprint Alterations5.2.1 Obliteration; 5.2.2 Distortion; 5.2.3 Imitation; 5.3 Related Work; 5.3.1 Orientation Field Analysis; 5.3.2 Minutiae Distribution Analysis; 5.4 Recent Algorithms for Fingerprint Alteration Detection; 5.4.1 Preprocessing; 5.4.2 Singular Point Density Analysis; 5.4.3 Minutia Orientation Analysis; 5.4.4 Orientation Difference Map; 5.4.5 Orientation Density Map; 5.5 Evaluation and Results; 5.6 Conclusion; References; Face and Video Analysis; 6 Face Sketch Recognition via Data-Driven Synthesis; 6.1 Introduction; 6.2 Related Work.…”
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Analytical modeling of wireless communication systems by Chiasserini, C. F. (Carla-Fabiani), Gribaudo, Marco, 1972-, Manini, Daniele

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Artificial Intelligence and Machine Learning Fundamentals : Develop Real-World Applications Powered by the Latest AI Advances. by Nagy, Zsolt

Table of Contents: “…Optimizing the Minmax Algorithm with Alpha-Beta PruningDRYing up the Minmax Algorithm -- The NegaMax Algorithm; Using the EasyAI Library; Activity 4: Connect Four; Summary; Regression; Introduction; Linear Regression with One Variable; What Is Regression?…”
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Applied data analytics : principles and applications by Agbinya, Johnson I.

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Ranking the Liveability of the World's Major Cities : the Global Liveable Cities Index (GLCI). by Tan, Khee Giap

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