Search Results - (((((((want OR wanton) OR semantic) OR when) OR cantor) OR anne) OR share) OR hints) algorithms.

F♯ for Machine Learning Essentials. by Mukherjee, Sudipta

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Advances in intelligent IT : active media technology 2006

Table of Contents: “…Title page; Preface; Organization; Contents; Keynotes; Active Media Technologies (AMT) from the Standpoint of the Wisdom Web; Anytime Learning and Classification for Online Applications; Domain-Driven Data Mining: Methodologies and Applications; Regular Papers; Web Service Request Transformatter; A Head-Tracker Based on the Lucas-Kanade Optical Flow Algorithm; A Fair Peer Selection Algorithm for an Ecommerce-Oriented Distributed Recommender System; Intelligent Decision Making with the Semantic Web; Relevence Assessment of Topic Ontology; Information Extraction from a Whole Web Site.…”
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Quantum Computation and Information. by Lomonaco, Samuel J.

Table of Contents: “…Vintage Simon quantum hidden subgroup algorithms (QHSAs)Part 5. Vintage Shor Algorithms -- 11. …”
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Challenges and opportunities of HealthGrids : proceedings of Healthgrid 2006

Table of Contents: “…Advancing of Russian ChemBioGrid by Bringing Data Management Tools into Collaborative EnvironmentGPS@ Bioinformatics Portal: From Network to EGEE Grid; Blast2GO Goes Grid: Developing a Grid-Enabled Prototype for Functional Genomics Analysis; Bioprofiling over Grid for eHealthcare; SARS Grid-An AG-Based Disease Management and Collaborative Platform; Knowledge Discovery on HealthGrids; A Secure Semantic Interoperability Infrastructure for Inter-Enterprise Sharing of Electronic Healthcare Records.…”
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Development of Modern Statistics and Related Topics : In Celebration of Prof Yaoting Zhang's 70th Birthday. by Zhang, Heping

Table of Contents: “…Estimation When Variances Are Known ; 4. Estimation when variances are Unknown ; 5. …”
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Differential and Differential-Algebraic Systems for the Chemical Engineer : Solving Numerical Problems. by Buzzi-Ferraris, Guido

Table of Contents: “…1.10 Adaptive Methods1.10.1 Method Derived from the Gauss-Kronrod Algorithm; 1.10.2 Method Derived from the Extended Trapezoid Algorithm; 1.10.3 Method Derived from the Gauss-Lobatto Algorithm; 1.11 Parallel Computations; 1.12 Classes for Definite Integrals; 1.13 Case Study: Optimal Adiabatic Bed Reactors for Sulfur Dioxide with Cold Shot Cooling; 2 Ordinary Differential Equations Systems; 2.1 Introduction; 2.2 Algorithm Accuracy; 2.3 Equation and System Conditioning; 2.4 Algorithm Stability; 2.5 Stiff Systems; 2.6 Multistep and Multivalue Algorithms for Stiff Systems.…”
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What intelligence tests miss : the psychology of rational thought by Stanovich, Keith E., 1950-

Table of Contents: “…Inside George W. Bush's mind : hints at what IQ tests miss -- Dysrationalia : separating rationality and intelligence -- The reflective mind, the algorithmic mind, and the autonomous mind -- Cutting intelligence down to size -- Why intelligent people doing foolish things is no surprise -- The cognitive miser : ways to avoid thinking -- Framing and the cognitive miser -- Myside processing : heads I win, tails I win too! …”
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Intelligent Technologies : From Theory to Applications - The New Trends in Computational Intelligence. by Kvasnicka, V.

Table of Contents: “…Neural Networks; Superconvergence Concept in Machine Learning; Application of the Parallel Population Learning Algorithm to Training Feed-forward ANN; Markovian Architectural Bias of Recurrent Neural Networks; SOFM Training Speedup; Generalized Forecasting Sigma-Pi Neural Network; Human Centered Intelligent Robots Using "Ontological Neural Network"; Trajectory Bounds of Solutions of Delayed Cellular Neural Networks Differential Systems; From Plain to Modular Topology: Automatic Modularization of Structured ANNs.…”
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Handbook of Mathematical Induction. by Gunderson, David S.

Table of Contents: “…Chapter 14: Logic and languageChapter 15: Graphs; Chapter 16: Recursion and algorithms; Chapter 17: Games and recreations; Chapter 18: Relations and functions; Chapter 19: Linear and abstract algebra; Chapter 20: Geometry; Chapter 21: Ramsey theory; Chapter 22: Probability and statistics; Part III: Solutions and hints to exercises; Chapter 23: Solutions: Foundations; Chapter 24: Solutions: Inductive techniques applied to the infinite; Chapter 25: Solutions: Paradoxes and sophisms; Chapter 26: Solutions: Empirical induction; Chapter 27: Solutions: Identities.…”
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Data Science : The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field

Table of Contents: “…Chapter 3 Working with Modern Data -- 3.1 Unstructured Data and Passive Collection -- 3.2 Data Types and Sources -- 3.3 Data Formats -- 3.3.1 CSV Files -- 3.3.2 JSON Files -- 3.3.3 XML and HTML -- 3.4 Databases -- 3.4.1 Relational Databases and Document Stores -- 3.4.2 Database Operations -- 3.5 Data Analytics Software Architectures -- 3.5.1 Shared Storage -- 3.5.2 Shared Relational Database -- 3.5.3 Document Store + Analytics RDB -- 3.5.4 Storage + Parallel Processing -- Chapter 4 Telling the Story, Summarizing Data -- 4.1 Choosing What to Measure…”
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Artificial intelligence : approaches, tools, and applications

Table of Contents: “…EVOLUTIONARY COMPUTING ; 2.1. Genetic Algorithms ; 2.2. Mechanism of a Genetic Algorithm ; 3. …”
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Cybernetic intelligent systems

Table of Contents: “…Cover ; Editorial advisory board ; Editorial ; Guest editorial ; HDR imaging for feature tracking in challenging visibility scenes ; A new classification strategy for human activity recognition using cost sensitive support vector machines for imbalanced data ; Coarticulation and speech synchronization in MPEG-4 based facial animation ; Semantic Category theory and Semantic Intertwine: the anathema of mathematics ; Tracking progress of African Peer Review Mechanism (APRM) using fuzzy comprehensive evaluation method.…”
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Text mining : applications and theory

Table of Contents: “…Automatic Keyword Extraction from Individual Documents / Stuart Rose, Dave Engel, Nick Cramer, Wendy Cowley -- Algebraic Techniques for Multilingual Document Clustering / Brett W Bader, Peter A Chew -- Content-Based Spam Email Classification using Machine-Learning Algorithms / Eric P Jiang -- Utilizing Nonnegative Matrix Factorization for Email Classification Problems / Andreas G K Janecek, Wilfried N Gansterer -- Constrained Clustering with -means Type Algorithms / Ziqiu Su, Jacob Kogan, Charles Nicholas -- Anomaly and Trend Detection. …”
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Advanced Analytics with R and Tableau. by Stirrup, Jen

Table of Contents: “…; Understanding the performance of the result; Next steps; Sharing our data analysis using Tableau; Interpreting the results; Summary; Chapter 5: Classifying Data with Tableau; Business understanding; Understanding the data; Data preparation; Describing the data; Data exploration; Modeling in R; Analyzing the results of the decision tree; Model deployment; Decision trees in Tableau using R; Bayesian methods; Graphs; Terminology and representations; Graph implementations; Summary.…”
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Advances in scalable web information integration and service : proceedings of DASFAA2007, Bangkok, Thailand, 9-12 April 2007

Table of Contents: “…Invited session. Using semantics in XML data management / Tok Wang Ling and Gillian Dobbie. …”
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Information modelling and knowledge bases XXX

Table of Contents: “…Intro; Title Page; Preface; Committees; Contents; Goal-Oriented Adaptive and Extensible Study-Process Creation with Optimal Cyclic-Learning in Graph-Structured Knowledge; A Process Calculus for Design and Modeling of Retro-Synthesis; The Discrete-Event Approach to Simulate Stochastic Multi-Product (r, Q) Inventory Control Systems; A Semantic Orthogonal Mapping Method Through Deep-Learning for Semantic Computing; Cultures in Information Systems Development; Clutter Reduction in Parallel Coordinates Using Aesthetic Criteria; Toward Manageable Data Sources…”
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Introduction to identity-based encryption by Martin, Luther

Table of Contents: “…Introduction -- Basic mathematical concepts and properties -- Properties of elliptic curves -- Divisors and the Tate pairing -- Cryptography and computational complexity -- Related cryptographic algorithms -- The Cocks IBE scheme -- Boneh-Franklin IBE -- Boneh-Boyen IBE -- Sakai-Kasahara IBE -- Hierarchial IBE and master secret sharing -- Calculating pairings.…”
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Integrated and collaborative product development environment : technologies and implementations by Li, W. D.

Table of Contents: “…Intelligent Optimisation of Process Planning; 5.1 Intelligent Optimisation Strategies for CAPP Systems; 5.2 Knowledge Representation of Process Plans; 5.2.1 Process plan representation; 5.2.2 Machining cost criteria for process plans; 5.2.3 Precedence constraints; 5.3 A Hybrid GA/SA-based Optimisation Method; 5.3.1 Overview of the algorithm; 5.3.2 Genetic algorithm -- phase 1; 5.3.3 Simulated annealing algorithm -- phase 2; 5.3.4 Constraint handling algorithm.…”
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Insight into Theoretical and Applied Informatics : Introduction to Information Technologies and Computer Science. by Yatsko, Andrzej

Table of Contents: “…1.2.7 Informatics and Economics1.3 Information Theory; 1.3.1 Quantities of Information; 1.3.1.1 Units for Measuring Computer Information; 1.3.1.2 Quantities of Information in Information Theory; 1.3.2 Coding Theory; 1.3.3 Semiotics; 1.3.3.1 Computational Semiotics; 2 Algorithmics; 2.1 The Science of Algorithms; 2.1.1 Algorithm Design; 2.1.2 Algorithmic Complexity Theory; 2.1.3 Algorithm Analysis; 2.2 Data Science (Datalogy); 2.2.1 Raw Data; 2.2.2 Data Structures; 2.2.3 Data Analysis (Data Analytics); 2.2.4 Data Mining; 3 Computer Programming; 3.1 Computer Programming Languages.…”
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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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