Search Results - (((((((ant OR wants) OR wants) OR wwanted) OR cantor) OR anne) OR giant) OR anting) algorithms.

There's Something about Gödel : The Complete Guide to the Incompleteness Theorem. by Berto, Francesco

Table of Contents: “…. -- 6 ... and the unsatisfied logicists, Frege and Russell -- 7 Bits of set theory -- 8 The Abstraction Principle -- 9 Bytes of set theory -- 10 Properties, relations, functions, that is, sets again -- 11 Calculating, computing, enumerating, that is, the notion of algorithm…”
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FX barrier options : a comprehensive guide for industry quants by Dadachanji, Zareer

Table of Contents: “…Stupid; 4.10 Five things we want from a model; 4.11 Stochastic volatility (SV) models; 4.11.1 SABR model; 4.11.2 Heston model; 4.12 Mixed local/stochastic volatility (lsv) models; 4.12.1 Term structure of volatility of volatility; 4.13 Other models and methods; 4.13.1 Uncertain Volatility (UV) models; 4.13.2 Jump-diffusion models; 4.13.3 Vanna-volga methods; 5 Smile Risk Management; 5.1 Black-Scholes with term structure; 5.2 Local volatility model; 5.3 Spot risk under smile models; 5.4 Theta risk under smile models; 5.5 Mixed local/stochastic volatility models; 5.6 Static hedging; 5.7 Managing risk across businesses; 6 Numerical Methods; 6.1 Finite-difference (FD) methods; 6.1.1 Grid geometry; 6.1.2 Finite-difference schemes; 6.2 Monte Carlo (MC) methods; 6.2.1 Monte Carlo schedules; 6.2.2 Monte Carlo algorithms; 6.2.3 Variance reduction; 6.2.4 The Brownian Bridge; 6.2.5 Early…”
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Cognitive Electronic Warfare : An Artificial Intelligence Approach. by Haigh, Karen

Table of Contents: “…-- 1.5 Reader's Guide -- 1.6 Conclusion -- References -- 2 Objective Function -- 2.1 Observables That Describe the Environment -- 2.1.1 Clustering Environments -- 2.2 Control Parameters to Change Behavior -- 2.3 Metrics to Evaluate Performance -- 2.4 Creating a Utility Function -- 2.5 Utility Function Design Considerations -- 2.6 Conclusion -- References -- 3 ML Primer -- 3.1 Common ML Algorithms -- 3.1.1 SVMs -- 3.1.2 ANNs -- 3.2 Ensemble Methods -- 3.3 Hybrid ML -- 3.4 Open-Set Classification -- 3.5 Generalization and Meta-learning -- 3.6 Algorithmic Trade-Offs -- 3.7 Conclusion -- References -- 4 Electronic Support -- 4.1 Emitter Classification and Characterization -- 4.1.1 Feature Engineering and Behavior Characterization -- 4.1.2 Waveform Classification -- 4.1.3 SEI -- 4.2 Performance Estimation -- 4.3 Multi-Intelligence Data Fusion -- 4.3.1 Data Fusion Approaches -- 4.3.2 Example: 5G Multi-INT Data Fusion for Localization -- 4.3.3 Distributed-Data Fusion -- 4.4 Anomaly Detection -- 4.5 Causal Relationships -- 4.6 Intent Recognition -- 4.6.1 Automatic Target Recognition and Tracking -- 4.7 Conclusion -- References -- 5 EP and EA -- 5.1 Optimization -- 5.1.1 Multi-Objective Optimization -- 5.1.2 Searching Through the Performance Landscape -- 5.1.3 Optimization Metalearning -- 5.2 Scheduling -- 5.3 Anytime Algorithms -- 5.4 Distributed Optimization -- 5.5 Conclusion.…”
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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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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 buildings, smart communities and demand response

Table of Contents: “…Demand Response in Smart Zero Energy Buildings and Grids / Nikos Kampelis -- DR in Smart and Near-zero Energy Buildings: The Leaf Community / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- Performance of Industrial and Residential Near-zero Energy Buildings / Nikos Kampelis, Konstantinos Gobakis, Vagias Vagias, Denia Kolokotsa, Laura Standardi, Daniela Isidori, Cristina Cristalli, Fabio Maria Montagnino, Filippo Paredes, Pietro Muratore, Luca Venezia, Marina Kyprianou Dracou, Alaric Montenon, Andri Pyrgou, Theoni Karlessi, Mattheos Santamouris -- HVAC Optimization Genetic Algorithm for Industrial Near-Zero Energy Building Demand Response / Nikos Kampelis, Nikolaos Sifakis, Denia Kolokotsa, Konstantinos Gobakis, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Smart Grid/Community Load Shifting GA Optimization Based on Day-ahead ANN Power Predictions / Nikos Kampelis, Elisavet Tsekeri, Denia Kolokotsa, Konstantinos Kalaitzakis, Daniela Isidori, Cristina Cristalli -- Conclusions and Recommendations.…”
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Numerical linear algebra with applications : using MATLAB by Ford, William H.

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Fast sequential Monte Carlo methods for counting and optimization by Rubinstein, Reuven Y.

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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…Scheduling a flowshop problem with fuzzy processing times using ant colony optimization / S. Kilic and C. Kahraman. …”
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Artificial Neural Network Applications for Software Reliability Prediction. by Bisi, Manjubala

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Artificial intelligent techniques for electric and hybrid electric vehicles

Table of Contents: “…2.6 PID Controller -- 2.7 Fuzzy Control -- 2.8 Auto-Tuning Type Fuzzy PID Controller -- 2.9 Genetic Algorithm -- 2.10 Artificial Neural Network-Based Controller -- 2.11 BLDC Motor Speed Controller With ANN-Based PID Controller -- 2.11.1 PID Controller-Based on Neuro Action -- 2.11.2 ANN-Based on PID Controller -- 2.12 Analysis of Different Speed Controllers -- 2.13 Conclusion -- References -- Chapter 3 Optimization Techniques Used in Active Magnetic Bearing System for Electric Vehicles -- 3.1 Introduction -- 3.2 Basic Components of an Active Magnetic Bearing (AMB) -- 3.2.1 Electromagnet Actuator…”
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Advanced Research on Material Engineering, Chemistry, Bioinformatics III : Selected, Peer Reviewed Papers from the 2013 3rd International Conference on Material Engineering, Chemis...

Table of Contents: “…Silicon Based Compound Filter Material Strengthening A/O Bio-Filter Denitrification Performance Synthesis and Application of Manganese Oxide Based Nanomaterials; Study on the Giant Magnetostrictive Materials with Actuator Displacement Model Parameter Identification; Experimental Study on Freeze Thaw Resistance Concrete; A Comparative Analysis of the Performance of PVC Film Aging by Ultraviolet and Thermal Methods; Effects on the Properties of Polyethylene Film Aging by Different Methods…”
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The bitcoin big bang : how alternative currencies are about to change the world by Kelly, Brian, 1971-

Table of Contents: “…; 8 Building the Nautiluscoin Economy; Dynamic Proof-of-Stake; Nautiluscoin Gross Domestic Product Target; Algorithmic Monetary Policy.…”
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Mastering Scala machine learning by Kozlov, Alexander

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Numerical models for submerged breakwaters : coastal hydrodynamics and morphodynamics by Ahmadian, Amir Sharif

Table of Contents: “…3 Literature Review and BackgroundReferences; 4 Theories and Methodologies; 4.1 Introduction; 4.2 Traditional Models for Water Waves; 4.3 New Approaches; 4.3.1 Meshless Methods; 4.3.2 Artificial Intelligence Methods; MLP Networks; Back-Propagation Algorithm; Levenberg-Marquardt Algorithm; RBF Networks; References; 5 Mathematical Modeling and Algorithm Development; 5.1 Navier-Stokes Equations; 5.2 The Turbulent Model; 5.3 Initial and Boundary Conditions; 5.4 Shallow Waters; 5.5 The Extended Mild-Slope Equation; 5.6 Boussinesq Equations; 5.7 Smoothed Particles Hydrodynamics.…”
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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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Compressed sensing : theory and applications

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Sentient Enterprise The Evolution of Business Decision-making.

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Better living through economics

Table of Contents: “…Introduction / John J Siegfried -- Overview: Highlights of the benefits of basic science in economics / Charles R Plott -- Comment / Daniel S Hamermesh -- Comment / Daniel Newlon -- 1: Evolution of emissions trading / Thomas H Tietenberg -- Comment / Wallace E Oates -- 2: Better living through improved price indexes / Michael J Boskin -- Comment / Jerry Hausman -- 3: Economics and the earned income tax credit / Robert A Moffitt -- Comment / V Joseph Hotz -- 4: Trade liberalization and growth in developing countries / Anne O Krueger -- Comment / Douglas A Irwin -- 5: Role of economics in the welfare-to-work reforms of the 1990s / Rebecca M Blank -- Comment / Nancy Folbre -- 6: Better living through monetary economics / John B Taylor -- Comment Laurence H Meyer -- 7: Greatest auction in history / R Preston McAfee, John McMillan, Simon Wilkie -- Comment / Jeremy Bulow -- 8: Air-transportation deregulation / Elizabeth E Bailey -- Comment / Nancy L Rose -- 9: Deferred-acceptance algorithms: history, theory, practice / Alvin E Roth -- Comment / Peter Cramton -- 9: Economics, economists, and antitrust: a tale of growing influence / Lawrence J White -- Comment / Kenneth G Elzinga -- 11: Economics and the all-volunteer military force / Beth J Asch, James C Miller III, John T Warner -- Comment / Walter Y Oi -- 12: Public policy and saving for retirement: the autosave features of the Pension Protection Act of 2006 / John Beshears [and others] -- Comment / Robert J Shiller -- Contributors -- Index.…”
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Regression Analysis with Python. by Massaron, Luca, Boschetti, Alberto

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