Search Results - (((((((ant OR wante) OR semantic) OR win) OR cantor) OR anne) OR shape) OR hints) algorithms.

Big Data, IoT, and Machine Learning : Tools and Applications. by Agrawal, Rashmi

Table of Contents: “…Chapter 3 Reviews Analysis of Apple Store Applications Using Supervised Machine Learning -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Machine Learning Algorithms -- 3.2.2 Feature Extraction Algorithms -- 3.3 Proposed Methodology -- 3.3.1 Data Collection -- 3.3.2 Feature Extraction -- 3.3.3 Data Analysis and Sentiment Analysis -- Text Processing -- 3.3.4 Text Normalisation -- 3.4 Feature Extraction Algorithm -- 3.4.1 CountVectorizer -- 3.4.2 TfidfVectorizer (TF-IDF) -- 3.5 Supervised ML Classification -- 3.6 Experiment Design -- 3.7 Experimental Results and Analysis…”
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Principles of artificial neural networks by Graupe, Daniel

Table of Contents: “…Fundamentals of biological neural networks -- ch. 3. Basic principles of ANNs and their early structures. 3.1. Basic principles of ANN design. 3.2. …”
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Knowledge discovery for business information systems

Table of Contents: “…Problem Description -- 3. The FUP Algorithm for the Insertion Only Case -- 4. The FUP Algorithm for the Deletions Only Case -- 5. …”
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Introduction to graph theory by Voloshin, Vitaly I. (Vitaly Ivanovich), 1954-

Table of Contents: “…What Is Mathematical Induction; 7.2. Graph Theory Algorithms and Their Complexity; 7.3. Answers and Hints to Selected Exercises; 7.4. …”
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In silico dreams : how artificial intelligence and biotechnology will create the medicines of the future by Hilbush, Brian

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Advanced Artificial Intelligence.

Table of Contents: “…Applications of temporal and spatial logic; 4.7.4. Randell algorithm; Exercises; Chapter 5 Case-Based Reasoning; 5.1 Overview; 5.2 Basic Notations; 5.3 Process Model; 5.4 Case Representation; 5.4.1 Semantic Memory Unit; 5.4.2 Memory Network; 5.5 Case Indexing; 5.6 Case Retrieval; 5.7 Similarity Relations in CBR; 5.7.1 Semantic similarity; 5.7.2 Structural similarity; 5.7.3 Goal's features; 5.7.4 Individual similarity; 5.7.5 Similarity assessment; 5.8 Case Reuse; 5.9 Case Retainion; 5.10 Instance-Based Learning.…”
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Applied Artificial Intelligence : Proceedings of the 7th International FLINS Conference. by Arena, Paolo, Fortuna, Luigi

Table of Contents: “…Integrated operations in arctic environments / F. wre. Can the semantic web be designed without using fuzzy logic? …”
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Applied mechatronics and android robotics : selected, peer reviewed papers from the International Conference on Applied Mechatronics and Android Robotics (ICAMAR 2013), July 13-14,...

Table of Contents: “…Applied Mechatronics and Android Robotics; Preface, Committees and Sponsors; Table of Contents; Chapter 1: Robot Design, Motion Planning and Robot Control; Design of a Master Manipulator with Dynamical Simplification for Master-Slave Robot; Parameter Self-Adjusting Path Tracking Algorithm of Mobile Robots; Global Path Planning for Mobile Robot Based on Improved Ant Colony Algorithms; A Study of Robot Platform Based on WiFi Remote Control; Fire Detection System Based-On Mobile Robots; Phase Plane Analysis of Battery Series Balancing on Robot…”
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Web-based learning : men and machines - proceedings of the first international conference on web-based learning in china (icwl 2002). by REGGIE, KWAN

Table of Contents: “…Patterns of Web Based Learning in the Semantic Web Era ; PART THREE Tools.…”
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Internet+ and electronic business in China : innovation and applications

Table of Contents: “…Emotional analysis of online reviews on e-business platforms -- Chapter 14. Semantic search of online reviews on e-business platforms -- Part IV. …”
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Deep Learning Quick Reference : Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. by Bernico, Michael

Table of Contents: “…Drawbacks to consider when using a neural network for regressionUsing deep neural networks for regression; How to plan a machine learning problem; Defining our example problem; Loading the dataset; Defining our cost function; Building an MLP in Keras; Input layer shape; Hidden layer shape; Output layer shape; Neural network architecture; Training the Keras model; Measuring the performance of our model; Building a deep neural network in Keras; Measuring the deep neural network performance; Tuning the model hyperparameters; Saving and loading a trained Keras model; Summary.…”
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Comorbidity in migraine

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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Technologies for Engineering Manufacturing Systems Control in Closed Loop. by Preuße, Sebastian

Table of Contents: “…Intro; 1 Introduction; 2 Basic Principles; 2.1 Technologies for studying Behavior; 2.2 Plants; 2.3 Controllers; 2.3.1 IEC 61131-3; 2.3.2 IEC 61499-1; 2.4 System Models; 2.4.1 Syntax; 2.4.2 Semantics; 2.5 Basics of Specifications; 2.5.1 Computation Tree Logic; 2.5.2 Extended Computation Tree Logic; 2.5.3 Timed Computation Tree Logic; 2.5.4 Symbolic Timing Diagrams; 2.6 Closed-Loop Composition; 2.7 Model Checking; 2.7.1 General Remarks; 2.7.2 Model Checking Algorithm; 2.8 Summary; 3 Formal Modeling of Plant, Controller, and the Closed Loop; 3.1 Demonstration Example; 3.2 Formal Plant Modeling.…”
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Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences by Song, Xin-Yuan

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Solutions in lidar profiling of the atmosphere by Kovalev, Vladimir A.

Table of Contents: “…1.6.1 Algorithm and Solution Uncertainty1.6.2 Numerical Simulations and Experimental Data; 1.7 Examination of the Remaining Offset in the Backscatter Signal by~Analyzing the Shape of the Integrated Signal; 1.8 Issues in the Examination of the Lidar Overlap Function; 1.8.1 Influence of Distortions in the Lidar Signal when Determining the~Overlap Function; 1.8.2 Issues of Lidar Signal Inversion within the Incomplete Overlap Area; Chapter 2 Essentials and Issues in Separating the Backscatter and Transmission Terms in The Lidar Equation.…”
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Information Theory Meets Power Laws : Stochastic Processes and Language Models. by Dębowski, Łukasz Jerzy, $$d 1975- $$e author

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Parts-feeding systems for assembly : organisation, logistics and automation

Table of Contents: “…A model for kitting operations planningRobust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods; Re-layout of an assembly area: a case study at Bosch Rexroth Oil Control; A simple mechanical measurement system for the posture evaluation of wing components using the PSO and ICP algorithms; Implementation framework for a fully flexible assembly system (F-FAS); A genetic algorithm for supermarket location problem; New Kanban model for tow-train feeding system design.…”
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3D face modeling, analysis and recognition by Daoudi, Mohamed, Ph. D., Srivastava, Anuj, Veltkamp, Remco C., 1963-

Table of Contents: “…3.5.4 Extension to Facial Surfaces Shape Analysis3.6 The Dense Scalar Field (DSF); 3.7 Statistical Shape Analysis; 3.7.1 Statistics on Manifolds: Karcher Mean; 3.7.2 Learning Statistical Models in Shape Space; 3.8 Applications of Statistical Shape Analysis; 3.8.1 3D Face Restoration; 3.8.2 Hierarchical Organization of Facial Shapes; 3.9 The Iso-geodesic Stripes; 3.9.1 Extraction of Facial Stripes; 3.9.2 Computing Relationships between Facial Stripes; 3.9.3 Face Representation and Matching Using Iso-geodesic Stripes; Exercises; Glossary; References.…”
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Wavelet theory approach to pattern recognition

Table of Contents: “…Comparison with other wavelets. 7.4. Algorithm and experiments -- ch. 8. Skeletonization of ribbon-like shapes with new wavelet function. 8.1. …”
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