Search Results - (((((((ant OR want) OR mkantis) OR when) OR cantor) OR anne) OR shape) OR hints) algorithms.

Pediatric incontinence : evaluation and clinical management

Full text (MFA users only)

Knowledge mining using intelligent agents

Full text (MFA users only)

Nonlinear parameter optimization using R tools by Nash, John C., 1947-

Table of Contents: “…Chapter 3 Software structure and interfaces3.1 Perspective; 3.2 Issues of choice; 3.3 Software issues; 3.4 Specifying the objective and constraints to the optimizer; 3.5 Communicating exogenous data to problem definition functions; 3.5.1 Use of ""global'' data and variables; 3.6 Masked (temporarily fixed) optimization parameters; 3.7 Dealing with inadmissible results; 3.8 Providing derivatives for functions; 3.9 Derivative approximations when there are constraints; 3.10 Scaling of parameters and function; 3.11 Normal ending of computations; 3.12 Termination tests-abnormal ending.…”
Full text (MFA users only)

Trading on sentiment : the power of minds over markets by Peterson, Richard L., 1972-

Full text (MFA users only)

Computer and network security essentials

Full text (MFA users only)

Remote Sensing Digital Image Analysis : an Introduction by Richards, John A.

Table of Contents: “…More Advanced Considerations -- 8.8 Context Classification -- 8.8.1 The Concept of Spatial Context -- 8.8.2 Context Classification by Image Pre-Processing -- 8.8.3 Post Classification Filtering -- 8.8.4 Probabilistic Label Relaxation -- 8.8.4.1 The Basic Algorithm -- 8.8.4.2 The Neighbourhood Function -- 8.8.4.3 Determining the Compatibility Coefficients -- 8.8.4.4 The Final Step -- Stopping the Process -- 8.8.4.5 Examples -- 8.9 Classification of Mixed Image Data -- 8.9.1 The Stacked Vector Approach -- 8.9.2 Statistical Methods -- 8.9.3 The Theory of Evidence -- 8.9.3.1 The Concept of Evidential Mass -- 8.9.3.2 Combining Evidence -- the Orthogonal Sum -- 8.9.3.3 Decision Rule -- 8.10 Classification Using Neural Networks -- 8.10.1 Linear Discrimination -- 8.10.1.1 Concept of a Weight Vector -- 8.10.1.2 Testing Class Membership -- 8.10.1.3 Training -- 8.10.1.4 Setting the Correction Increment -- 8.10.1.5 Classification -- The Threshold Logic Unit -- 8.10.1.6 Multicategory Classification -- 8.10.2 Networks of Classifiers -- Solutions of Nonlinear Problems -- 8.10.3 The Neural Network Approach -- 8.10.3.1 The Processing Element -- 8.10.3.2 Training the Neural Network -- Backpropagation -- 8.10.3.3 Choosing the Network Parameters -- 8.10.3.4 Examples -- References for Chapter 8 -- Problems -- 9 -- Clustering and Unsupervised Classification -- 9.1 Delineation of Spectral Classes -- 9.2 Similarity Metrics and Clustering Criteria -- 9.3 The Iterative Optimization (Migrating Means) Clustering Algorithm -- 9.3.1 The Basic.…”
Full text (MFA users only)

Introduction to numerical electrostatics using MATLAB by Dworsky, Lawrence N., 1943-

Full text (MFA users only)

Concise dictionary of engineering : a guide to the language of engineering by Hanifan, Ronald E.

Full text (MFA users only)

Artificial intelligence in society.

Full text (MFA users only)

Tracking with particle filter for high-dimensional observation and state spaces by Dubuisson, Séverine

Table of Contents: “…Application to tracking shapes; 3.4. Conjoint estimation of dynamic and static parameters; 3.5. …”
Full text (MFA users only)

Diatoms : ecology and life cycle

Table of Contents: “…""DIATOMS: ECOLOGY AND LIFE CYCLE""; ""DIATOMS: ECOLOGY AND LIFE CYCLE""; ""Contents""; ""Preface""; ""The Role of Environmental Factors in Shaping Diatom Frustule: Morphological Plasticity and Teratological Forms""; ""Abstract""; ""1. …”
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)

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.…”
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)

Building Machine Learning Systems with Python : Explore Machine Learning and Deep Learning Techniques for Building Intelligent Systems Using Scikit-Learn and TensorFlow, 3rd Editio... by Pedro Coelho, Luis

Table of Contents: “…Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Python Machine Learning; Machine learning and Python -- a dream team; What the book will teach you -- and what it will not; How to best read this book; What to do when you are stuck; Getting started; Introduction to NumPy, SciPy, Matplotlib, and TensorFlow; Installing Python; Chewing data efficiently with NumPy and intelligently with SciPy; Learning NumPy; Indexing; Handling nonexistent values; Comparing the runtime; Learning SciPy; Fundamentals of machine learning.…”
Full text (MFA users only)

ModSecurity 2.5. by Mischel, Magnus

Table of Contents: “…Filtering collection fields using a regular expressionBuilt-in fields; Transformation functions; Other operators; Set-based pattern matching with @pm and @pmFromFile; @pmFromFile; Performance of the phrase matching operators; Validating character ranges; Phases and rule ordering; Actions-what to do when a rule matches; Allowing requests; Blocking requests; Taking no action but continuing rule processing; Dropping requests; Redirecting and proxying requests; SecAction; Using the ctl action to control the rule engine; How to use the ctl action; Macro expansion; SecRule in practice.…”
Full text (MFA users only)

Digitalization of society and socio-political issues. 2, Digital, information, and research

Table of Contents: “…6. When Vlogging Educates in Politics: The French Case of "Osons Causer" -- 6.1. …”
Full text (MFA users only)

Moments and moment invariants in pattern recognition by Flusser, Jan

Full text (MFA users only)

Advanced Computational Electromagnetic Methods. by Yu, Wenhua

Table of Contents: “…1.7.1 A Symmetric Reflector Antenna -- 1.7.2 A Symmetric Reflector Antenna with an Elliptical Projected Aperture -- 1.7.3 Near-Field Prediction with Only Two Pattern Cuts -- 2.6.1 Planar PEC Boundaries -- 2.6.3 Critical Curved PEC Models -- 2.7.1 The Finite Volumes-Based FV24 Algorithm -- 2.7.2 High-Order Algorithms for Compact-FDTD Grids -- 3.2.1 Features of the FDTD Code -- 3.2.2 Input Parameters File -- 3.2.3 Main Program Layout -- 3.2.4 Field Updates -- 3.2.5 Outputs of the Program -- 3.3.1 Performance Optimization -- 3.3.2 Memory Accesses -- 3.3.3 Preparation of the GPU Device -- 3.3.4 Thread to Cell Mapping -- 3.3.5 The Time-Marching Loop -- 3.3.6 Field Updates -- 3.3.7 Source Updates and Output Calculations -- 4.3.1 FDTD Space Lattice -- 4.3.2 Example Updating Algorithm for TM Grid Cells -- 4.4.1 Collisional Plasma Algorithm -- 4.4.2 Two Example Validations -- 4.4.3 Summary of Performance -- 4.5.1 Overview -- 4.5.2 Mean Field Equations -- 4.5.3 Variance Field Equations -- 5.2.1 Hardware Configuration -- 5.2.2 Software Configuration -- 5.2.3 Compilation Environment -- 5.3.1 Performance Optimization -- 5.3.2 Memory Alignment -- 5.3.3 Parallel FDTD Implementation -- 5.3.4 Job Scheduling Strategy -- 5.3.5 FDTD Code Development -- 5.3.6 Matrix Multiplication -- 6.1.1 FETI Method with One Lagrange Multiplier -- 6.1.2 FETI Method with Two Lagrange Multipliers -- 6.1.3 Symbolic Formulation -- 6.2.1 FETI-DP Method with One Lagrange Multiplier -- 6.2.2 FETI-DP Method with Two Lagrange Multipliers -- 6.2.3 Comparison Between FETI-DP Methods with One and Two Lagrange Multipliers -- 6.3.1 Nonconformal Interface and Conformal Corner Meshes -- 6.3.2 Extension to Nonconformal Interface and Corner Meshes -- 6.4.1 Nonconformal Interface and Conformal Corner Meshes -- 6.4.2 Extension to Nonconformal Interface and Corner Meshes.…”
Full text (MFA users only)

Mastering OpenCV with Practical Computer Vision Projects. by Emami, Shervin

Table of Contents: “…Reviewing the Android appCartoonifying the image when the user taps the screen; Saving the image to a file and to the Android picture gallery; Showing an Android notification message about a saved image; Changing cartoon modes through the Android menu bar; Reducing the random pepper noise from the sketch image; Showing the FPS of the app; Using a different camera resolution; Customizing the app; Summary; 2. …”
Full text (MFA users only)