Search Results - (((((((ant OR wants) OR semantic) OR wind) OR cantor) OR anne) OR plans) OR hints) algorithms.

Oracle SOA Suite 11g Performance Cookbook. by Brasier, Matthew

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Curves and Surfaces for CAGD : a Practical Guide. by Farin, Gerald

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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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Perspective in image-guided surgery : proceedings of the Scientific Workshop on Medical Robotics, Navigation, and Visualization : RheinAhrCampus Remagen, Germany, 11-12 March

Table of Contents: “…Stereotactic treatment planning using fused multi-modality imaging / K.-D. …”
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Building Machine Learning Systems with Python. by Richert, Willi

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Functional Applications of Text Analytics Systems by Simske, Steven

Table of Contents: “…Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgement -- List of Figures -- List of Tables -- List of Abbreviations -- 1: Linguistics and NLP -- 1.1 Introduction -- 1.2 General Considerations -- 1.3 Machine Learning Aspects -- 1.3.1 Machine Learning Features -- 1.3.2 Other Machine Learning Approaches -- 1.4 Design/System Considerations -- 1.4.1 Sensitivity Analysis -- 1.4.2 Iterative Tradeoff in Approach -- 1.4.3 Competition -- Cooperation Algorithms -- 1.4.4 Top-Down and Bottom-Up Designs -- 1.4.5 Agent-Based Models and Other Simulations -- 1.5 Applications/Examples -- 1.6 Test and Configuration -- 1.7 Summary -- 2: Summarization -- 2.1 Introduction -- 2.2 General Considerations -- 2.2.1 Summarization Approaches -- An Overview -- 2.2.2 Weighting Factors in Extractive Summarization -- 2.2.3 Other Considerations in Extractive Summarization -- 2.2.4 Meta-Algorithmics and Extractive Summarization -- 2.3 Machine Learning Aspects -- 2.4 Design/System Considerations -- 2.5 Applications/Examples -- 2.6 Test and Configuration -- 2.7 Summary -- 3: Clustering, Classification, and Categorization -- 3.1 Introduction -- 3.1.1 Clustering -- 3.1.2 Regularization -- An Introduction -- 3.1.3 Regularization and Clustering -- 3.2 General Considerations -- 3.3 Machine Learning Aspects -- 3.3.1 Machine Learning and Clustering -- 3.3.2 Machine Learning and Classification -- 3.3.3 Machine Learning and Categorization -- 3.4 Design/System Considerations -- 3.5 Applications/Examples -- 3.5.1 Query-Synonym Expansion -- 3.5.2 ANOVA, Cross-Correlation, and Image Classification -- 3.6 Test and Configuration -- 3.7 Summary -- 4: Translation -- 4.1 Introduction -- 4.2 General Considerations -- 4.2.1 Review of Relevant Prior Research -- 4.2.2 Summarization as a Means to Functionally Grade the Accuracy of Translation.…”
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Machine Learning in Chemical Safety and Health : Fundamentals with Applications. by Wang, Qingsheng

Table of Contents: “…Chapter 3 Flammability Characteristics Prediction Using QSPR Modeling -- 3.1 Introduction -- 3.1.1 Flammability Characteristics -- 3.1.2 QSPR Application -- 3.1.2.1 Concept of QSPR -- 3.1.2.2 Trends and Characteristics of QSPR -- 3.2 Flowchart for Flammability Characteristics Prediction -- 3.2.1 Dataset Preparation -- 3.2.2 Structure Input and Molecular Simulation -- 3.2.3 Calculation of Molecular Descriptors -- 3.2.4 Preliminary Screening of Molecular Descriptors -- 3.2.5 Descriptor Selection and Modeling -- 3.2.6 Model Validation -- 3.2.6.1 Model Fitting Ability Evaluation -- 3.2.6.2 Model Stability Analysis -- 3.2.6.3 Model Predictivity Evaluation -- 3.2.7 Model Mechanism Explanation -- 3.2.8 Summary of QSPR Process -- 3.3 QSPR Review for Flammability Characteristics -- 3.3.1 Flammability Limits -- 3.3.1.1 LFLT and LFL -- 3.3.1.2 UFLT and UFL -- 3.3.2 Flash Point -- 3.3.3 Auto-ignition Temperature -- 3.3.4 Heat of Combustion -- 3.3.5 Minimum Ignition Energy -- 3.3.6 Gas-liquid Critical Temperature -- 3.3.7 Other Properties -- 3.4 Limitations -- 3.5 Conclusions and Future Prospects -- References -- Chapter 4 Consequence Prediction Using Quantitative Property-Consequence Relationship Models -- 4.1 Introduction -- 4.2 Conventional Consequence Prediction Methods -- 4.2.1 Empirical Method -- 4.2.2 Computational Fluid Dynamics (CFD) Method -- 4.2.3 Integral Method -- 4.3 Machine Learning and Deep Learning-Based Consequence Prediction Models -- 4.4 Quantitative Property-Consequence Relationship Models -- 4.4.1 Consequence Database -- 4.4.2 Property Descriptors -- 4.4.3 Machine Learning and Deep Learning Algorithms -- 4.5 Challenges and Future Directions -- References -- Chapter 5 Machine Learning in Process Safety and Asset Integrity Management -- 5.1 Opportunities and Threats -- 5.2 State-of-the-Art Reviews -- 5.2.1 Artificial Neural Networks (ANNs).…”
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How Systems Form and How Systems Break : a Beginner's Guide for Studying the World. by Ren, Chiang H.

Table of Contents: “…2.2 Associations: Connections Between System Parts2.2.1 Discovery by Fuzzy Logic; 2.2.2 Discovery by Bayesian Networks; 2.2.3 Discovery by Rough Set Theory; 2.2.4 Discovery by Genetic Algorithms; 2.2.5 Discovery by Neural Networks; 2.2.6 Discovery by Agent-Based Modeling; 2.3 Structure: All Parts and Associations in the System; 2.3.1 Fixed and Firm Structures; 2.3.2 Clustered and Morphing Structures; 2.3.3 Dynamically Linking Structures; 2.3.4 Dynamically Influencing Structures; 2.4 Boundaries: How to Define the System; 2.4.1 Methodology of Iterating a Conceptual System Model.…”
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Theoretical Computer Science : Proceedings of the 10th Italian Conference on ICTCS '07. by Italiano, Giuseppe F.

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Database technology for life sciences and medicine

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Graph drawing and applications for software and knowledge engineers by Sugiyama, Kozo, 1945-

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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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Mobile magic : the saatchi and saatchi guide to mobile marketing and design by Eslinger, Tom

Table of Contents: “…Creepy; Section 3: Getting Going; Chapter 8: How to Budget; The Two Components of a Mobile Budget; How Much Money Should I Plan to Spend on Mobile?; Taking Inventory of Your Mobile Infrastructure.…”
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COMPUTATIONAL FINANCE : matlab (r) oriented modeling. by CESARONE, FRANCESCO

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Practical data analysis by Cuesta, Hector

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Stochastic filtering with applications in finance by Bhar, Ramaprasad

Table of Contents: “…Economic convergence in a filtering framework. 3.3. Ex-ante equity risk premium. 3.4. Concluding remarks -- 4. …”
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Design optimization of fluid machinery : applying computational fluid dynamics and numerical optimization by Kim, Kwang-Yong, 1956-, Samad, Abdus, Benini, Ernesto

Table of Contents: “…2.2.5.3 Periodic/Cyclic Boundary Conditions2.2.5.4 Symmetry Boundary Conditions; 2.2.6 Moving Reference Frame (MRF); 2.2.7 Verification and Validation; 2.2.8 Commercial CFD Software; 2.2.9 Open Source Codes; 2.2.9.1 OpenFOAM; References; Chapter 3 Optimization Methodology; 3.1 Introduction; 3.1.1 Engineering Optimization Definition; 3.1.2 Design Space; 3.1.3 Design Variables and Objectives; 3.1.4 Optimization Procedure; 3.1.5 Search Algorithm; 3.2 Multi-Objective Optimization (MOO); 3.2.1 Weighted Sum Approach; 3.2.2 Pareto-Optimal Front…”
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Software engineering for embedded systems : methods, practical techniques, and applications

Table of Contents: “…-- Examples of modeling languages -- The V diagram promise -- So, why would you want to model your embedded system? -- When should you model your embedded system? …”
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Architecture-aware optimization strategies in real-time image processing by Li, Chao, Balla-Arabe, Souleymane, Yang, Fan

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Robotics : science and systems VII

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