Search Results - (((((((kant OR wikant) OR semantic) OR wien) OR cantor) OR anne) OR shape) OR hints) algorithms.

Windows 2000 Graphics API Black Book. by Chandler, Damon

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Learning-based local visual representation and indexing by Rongrong, Ji, Yao, Hongxun, Gao, Yue, Duan, Ling-Yu, Dai, Qionghai

Table of Contents: “…3.4.1 Cross-database Case3.4.2 Incremental Transfer; 3.5 Experiments; 3.5.1 Quantitative results; 3.6 Summary; Chapter 4: Supervised Dictionary Learning via Semantic Embedding ; 4.1 Introduction; 4.2 Semantic Labeling Propagation; 4.2.1 Density Diversity Estimation ; 4.3 Supervised Dictionary Learning; 4.3.1 Generative Modeling ; 4.3.2 Supervised Quantization ; 4.4 Experiments; 4.4.1 Database and Evaluations; 4.4.2 Quantitative Results; 4.5 Summary; Chapter 5: Visual Pattern Mining; 5.1 Introduction; 5.2 Discriminative 3D Pattern Mining; 5.2.1 The Proposed Mining Scheme.…”
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Media Backends : Digital Infrastructures and Sociotechnical Relations. by Parks, Lisa

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New developments in lasers and electro-optics research

Table of Contents: “…Cho -- Shape detection by means of a laser line and approximation neural networks / J. …”
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Digital workflows in architecture : designing design -- designing assembly -- designing industry

Table of Contents: “…DIGITAL CRAFTSMANSHIP: FROM THINKING TO MODELING TO BUILDINGWireframe Algorithms (Editor's Notes); ALGORITHMIC WORKFLOWS IN ASSOCIATIVE MODELING; Workflow Teams (Editor's Notes); WORKFLOW CONSULTANCY; THE SCENT OF THE SYSTEM; indeterminacy (Editor's Notes); DESIGNING INDUSTRY; WHAT DO WE MEAN BY BUILDING DESIGN?…”
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Vision Geometry. by Melter, Robert A.

Table of Contents: “…Star-Shapedness of Digitized Planar ShapesAlgorithms for the Decomposition of Convex Polygons -- Decomposition of Discrete Curves into Piecewise Straight Segments in Linear Time -- Digitization Schemes and the Recognition of Digital Straight Lines, Hyperplanes, and Flats in Arbitrary Dimensions -- Computational Geometry and Computer Vision -- Convexity, Visibility, and Orthogonal Polygons…”
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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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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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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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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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Computational number theory : proceedings of the Colloquium on Computational Number Theory held at Kossuth Lajos University, Debrecen (Hungary), September 4-9, 1989

Table of Contents: “…On the solution of the diophantine equation Gn = P(x) with sieve algorithmOn Thue equations associated with certain quartic number fields; KANT -- a tool for computations in algebraic number fields; SIMATH -- a computer algebra system…”
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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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