Search Results - (((((((kent OR akant) OR wants) OR markant) OR cantor) OR anne) OR slave) OR hints) algorithms.

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…”
Full text (MFA users only)

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.…”
Full text (MFA users only)

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…”
Full text (MFA users only)

Java Data Mining - Strategy, Standard, and Practice : a Practical Guide for architecture, design, and implementation. by Hornick, Mark F.

Full text (MFA users only)

Biomedical image analysis recipes in MATLAB : for life scientists and engineers by Reyes-Aldasoro, Constantino Carlos

Full text (MFA users only)

Advanced Analytics with R and Tableau. by Stirrup, Jen

Full text (MFA users only)

Swift 2 design patterns : build robust and scalable iOS and Mac OS X game applications by Lange, Julien

Full text (MFA users only)

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.…”
Full text (MFA users only)

Energy storage for sustainable microgrid by Gao, David Wenzhong

Table of Contents: “…1.2.4.2 Weighted Least Squares Estimation1.2.4.3 Newton-Raphson Algorithm; 1.3 Microgrid Control Methods; 1.3.1 PQ Control; 1.3.2 V/f Control; 1.3.3 Droop Control; 1.3.3.1 Active Power Control; 1.3.3.2 Voltage Control; 1.4 Control Architectures in Microgrids; 1.4.1 Master-Slave Control; 1.4.2 Peer-to-peer Control; 1.4.3 Hierarchy Control; 1.5 Microgrid Protection; 1.6 Three-Phase Circuit for Grid-Connected DG; 1.6.1 LC Filter; 1.6.2 Isolation Transformer; 1.7 Energy Storage Technology in Renewable Microgrids; 1.7.1 Batteries; 1.7.1.1 Lead-Acid Batteries; 1.7.1.2 Lithium-Ion Batteries.…”
Full text (MFA users only)

Pediatric incontinence : evaluation and clinical management

Full text (MFA users only)

CUDA Application Design and Development. by Farber, Rob

Table of Contents: “…Multiple GPUs -- Explicit Synchronization -- Implicit Synchronization -- The Unified Virtual Address Space -- A Simple Example -- Profiling Results -- Out-of-Order Execution with Multiple Streams -- Tip for Concurrent Kernel Execution on the Same GPU -- Atomic Operations for Implicitly Concurrent Kernels -- Tying Data to Computation -- Manually Partitioning Data -- Mapped Memory -- How Mapped Memory Works -- Summary -- 8 CUDA for All GPU and CPU Applications -- Pathways from CUDA to Multiple Hardware Backends -- The PGI CUDA x86 Compiler -- The PGI CUDA x86 Compiler -- An x86 core as an SM -- The NVIDIA NVCC Compiler -- Ocelot -- Swan -- MCUDA -- Accessing CUDA from Other Languages -- SWIG -- Copperhead -- EXCEL -- MATLAB -- Libraries -- CUBLAS -- CUFFT -- MAGMA -- phiGEMM Library -- CURAND -- Summary -- 9 Mixing CUDA and Rendering -- OpenGL -- GLUT -- Mapping GPU Memory with OpenGL -- Using Primitive Restart for 3D Performance -- Introduction to the Files in the Framework -- The Demo and Perlin Example Kernels -- The Demo Kernel -- The Demo Kernel to Generate a Colored Sinusoidal Surface -- Perlin Noise -- Using the Perlin Noise Kernel to Generate Artificial Terrain -- The simpleGLmain.cpp File -- The simpleVBO.cpp File -- The callbacksVBO.cpp File -- Summary -- 10 CUDA in a Cloud and Cluster Environments -- The Message Passing Interface (MPI) -- The MPI Programming Model -- The MPI Communicator -- MPI Rank -- Master-Slave -- Point-to-Point Basics -- How MPI Communicates -- Bandwidth -- Balance Ratios -- Considerations for Large MPI Runs -- Scalability of the Initial Data Load -- Using MPI to Perform a Calculation -- Check Scalability -- Cloud Computing -- A Code Example -- Data Generation -- Summary -- 11 CUDA for Real Problems -- Working with High-Dimensional Data -- PCA/NLPCA -- Multidimensional Scaling -- K-Means Clustering.…”
Full text (MFA users only)

Digital wealth : an automatic way to invest successfully by Moore, Simon, 1978-

Full text (MFA users only)

Digitalization of Society and Socio-Political Issues. 1, Digital, Communication, and Culture

Table of Contents: “…The Digitalization of Cultural Policies in France 149; Anne BELLON 14.1.…”
Full text (MFA users only)

Integration of swarm intelligence and artificial neural network

Full text (MFA users only)

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

Full text (MFA users only)

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? …”
Full text (MFA users only)

Building Machine Learning Systems with Python. by Richert, Willi

Full text (MFA users only)

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).…”
Full text (MFA users only)

Database technology for life sciences and medicine

Full text (MFA users only)

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

Full text (MFA users only)