Search Results - (((((((kant OR wants) OR semantic) OR want) OR cantor) OR anne) OR shape) OR mantis) algorithms.

Algorithms of education : how datafication and artificial intelligence shape policy / by Gulson, Kalervo N., Sellar, Sam, Webb, P. Taylor

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Theory of randomized search heuristics : foundations and recent developments

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The rise of the algorithms : how YouTube and TikTok conquered the world by Jordan, John M.

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Algorithmic composition : a guide to composing music with Nyquist by Simoni, Mary Hope

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Multi-Objective Optimization in Theory and Practice II : metaheuristic algorithms. by Keller, André A.

Table of Contents: “…Near Pareto-Optimal Front; 1.3.4. Shapes of a Pareto-Optimal Front; 1.4. Selection Procedures of Algorithms; 1.4.1. …”
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Pathway modeling and algorithm research

Table of Contents: “…PATHWAY MODELING AND ALGORITHM RESEARCH; PATHWAY MODELING AND ALGORITHM RESEARCH; Contents; Preface; Biological Pathways and Their Modeling; Abstract; 1. …”
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An Introduction to Algorithmic Trading : Basic to Advanced Strategies. by Leshik, Edward, Cralle, Jane

Table of Contents: “…Introduction to trading algorithms. History ; All about trading algorithms you ever wanted to know ... ; Algos defined and explained ; Who uses and provides algos ; Why have they become mainstream so quickly? …”
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Statistical models of shape : optimisation and evaluation by Davies, Rhodri (Rhodri H.)

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Semantic matchmaking with nonmonotonic description logics by Grimm, Stephan

Table of Contents: “…Nonmonotonic Reasoning in the Semantic WebNonmonotonic Description Logics; Autoepistemic Description Logics; Formal Semantics for Autoepistemic DLs; Reasoning with Epistemic Operators; Circumscriptive Description Logics; Formal Semantics for Circumscriptive DLs; Reasoning with Circumscribed Knowledge Bases; Terminological Defaults; Discussion; A Reasoning Algorithm for Circumscriptive Description Logics; Principle Idea of a Tableaux Extension; Extending DL Tableaux Calculi by Preference Clashes; Restrictions on the Formalism; Deciding Circumscriptive ALCO…”
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Scalable algorithms for contact problems by Dostál, Zdeněk, Kozubek, Tomáš, 1975-, Sadowská, Marie, Vonk, Vít

Table of Contents: “…Optimal QP and QCQP Algorithms -- 5. Conjugate Gradients -- 6. Gradient Projection for Separable Convex Sets -- 7. …”
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Number, Shape, & Symmetry : an Introduction to Number Theory, Geometry, and Group Theory. by Herrmann, Diane L.

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Graph algorithms and applications 3

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Data management in the semantic web

Table of Contents: “…DATA MANAGEMENT IN THE SEMANTIC WEB ; DATA MANAGEMENT IN THE SEMANTIC WEB ; CONTENTS ; PREFACE ; INTERPRETATIONS OF THE WEB OF DATA; Abstract; 1. …”
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Introduction to programming : learn to program in Java with data structures, algorithms, and logic by Samoylov, Nick

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Learning OWL class expressions by Lehmann, Jens, 1982-

Table of Contents: “…Refinement Operator Based OWL Learning Algorithms; OCEL (OWL Class Expression Learner); Redundancy Elimination; Creating a Full Learning Algorithm; ELTL (EL Tree Learner)…”
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Graph partitioning and graph clustering : 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA

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Natural language processing : semantic aspects by Kapetanios, Epaminondas, Tatar, Doina, 1943-, Sacarea, Christian

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Nearest-neighbor methods in learning and vision : theory and practice

Table of Contents: “…Clarkson -- Locality-sensitive hashing using stable distributions / Alexandr Andoni [and others] -- New algorithms for efficient high-dimensional nonparametric classification / Ting Liu, Andrew W. …”
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Building winning algorithmic trading systems : a trader's journey from data mining to Monte Carlo simulation to live trading by Davey, Kevin J., 1966-

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3D Shape Analysis : Fundamentals, Theory and Applications. by Tabia, Hedi

Table of Contents: “…7.2 Problem Formulation7.3 Mathematical Tools; 7.4 Isometric Correspondence and Registration; 7.5 Nonisometric (Elastic) Correspondence and Registration; 7.6 Summary and Further Reading; 8 Semantic Correspondences; 8.1 Introduction; 8.2 Mathematical Formulation; 8.3 Graph Representation; 8.4 Energy Functions for Semantic Labeling; 8.5 Semantic Labeling; 8.6 Examples; 8.7 Summary and Further Reading; Part IV: Applications; 9 Examples of 3D Semantic Applications; 9.1 Introduction; 9.2 Semantics: Shape or Status; 9.3 Semantics: Class or Identity; 9.4 Semantics: Behavior; 9.5 Semantics: Position…”
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