Search Results - (((((((want OR when) OR semantic) OR art) OR cantor) OR anne) OR maarten) OR santis) algorithms.

There's Something about Gödel : The Complete Guide to the Incompleteness Theorem. by Berto, Francesco

Table of Contents: “…. -- 6 ... and the unsatisfied logicists, Frege and Russell -- 7 Bits of set theory -- 8 The Abstraction Principle -- 9 Bytes of set theory -- 10 Properties, relations, functions, that is, sets again -- 11 Calculating, computing, enumerating, that is, the notion of algorithm…”
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Optics and artificial vision by González-Acuäna, Rafael G., Chaparro-Romo, Héctor A., Melendez-Montoya, Israel

Table of Contents: “…Introduction -- 3.2. The Lucas-Kanade algorithm -- 3.3. Application of the Lucas-Kanade algorithm and its Python code -- 3.4. …”
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Handbook on semidefinite, conic and polynomial optimization

Table of Contents: “…Lasserre and Mihai Putinar -- Self-regular interior-point methods for semidefinite optimization / Maziar Salahi and Tamás Terlaky -- Elementary optimality conditions for nonlinear SDPs / Florian Jarre -- Recent progress in interior-point methods: cutting-plane algorithms and warm starts / Alexander Engau -- Exploiting sparsity in SDP relaxation of polynomial optimization problems / Sunyoung Kim and Masakazu Kojima -- Block coordinate descent methods for semidefinite programming / Zaiwen Wen, Donald Goldfarb, and Katya Scheinberg -- Projection methods in conic optimization / Didier Henrion and Jérôme Malick -- SDP relaxations for non-commutative polynomial optimization / Miguel Navascués, Stefano Pironio, and Antonio Acín -- Semidefinite programming and constraint programming / Willem-Jan van Hoeve -- The state-of-the-art in conic optimization software / Hans D. …”
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In pursuit of the traveling salesman : mathematics at the limits of computation by Cook, William, 1957-

Table of Contents: “…Humans versus computers -- Tour-finding strategies -- The TSP in neuroscience -- Animals solving the TSP -- Aesthetics -- Julian Lethbridge -- Jordan curves -- Continuous lines -- Art and mathematics -- Pushing the limits.…”
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The virtual life of film by Rodowick, David Norman

Table of Contents: “…Film begets video -- The death of cinema and the birth of film studies -- A medium in all things -- Automatisms and art -- Automatism and photography -- Succession and the film strip -- Ways of worldmaking -- A world past -- An ethics of time -- III. …”
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The Rise of Technosocialism : How Inequality, AI and Climate Will Usher in a New World. by King, Brett

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Cognitive Electronic Warfare : An Artificial Intelligence Approach. by Haigh, Karen

Table of Contents: “…-- 1.5 Reader's Guide -- 1.6 Conclusion -- References -- 2 Objective Function -- 2.1 Observables That Describe the Environment -- 2.1.1 Clustering Environments -- 2.2 Control Parameters to Change Behavior -- 2.3 Metrics to Evaluate Performance -- 2.4 Creating a Utility Function -- 2.5 Utility Function Design Considerations -- 2.6 Conclusion -- References -- 3 ML Primer -- 3.1 Common ML Algorithms -- 3.1.1 SVMs -- 3.1.2 ANNs -- 3.2 Ensemble Methods -- 3.3 Hybrid ML -- 3.4 Open-Set Classification -- 3.5 Generalization and Meta-learning -- 3.6 Algorithmic Trade-Offs -- 3.7 Conclusion -- References -- 4 Electronic Support -- 4.1 Emitter Classification and Characterization -- 4.1.1 Feature Engineering and Behavior Characterization -- 4.1.2 Waveform Classification -- 4.1.3 SEI -- 4.2 Performance Estimation -- 4.3 Multi-Intelligence Data Fusion -- 4.3.1 Data Fusion Approaches -- 4.3.2 Example: 5G Multi-INT Data Fusion for Localization -- 4.3.3 Distributed-Data Fusion -- 4.4 Anomaly Detection -- 4.5 Causal Relationships -- 4.6 Intent Recognition -- 4.6.1 Automatic Target Recognition and Tracking -- 4.7 Conclusion -- References -- 5 EP and EA -- 5.1 Optimization -- 5.1.1 Multi-Objective Optimization -- 5.1.2 Searching Through the Performance Landscape -- 5.1.3 Optimization Metalearning -- 5.2 Scheduling -- 5.3 Anytime Algorithms -- 5.4 Distributed Optimization -- 5.5 Conclusion.…”
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Three Views of Logic : Mathematics, Philosophy, and Computer Science. by Loveland, Donald W.

Table of Contents: “…Proof Theory; 1 Propositional Logic; 1.1 Propositional Logic Semantics; 1.2 Syntax: Deductive Logics; 1.3 The Resolution Formal Logic; 1.4 Handling Arbitrary Propositional Wffs; 2 Predicate Logic; 2.1 First-Order Semantics; 2.2 Resolution for the Predicate Calculus; 2.2.1 Substitution; 2.2.2 The Formal System for Predicate Logic; 2.2.3 Handling Arbitrary Predicate Wffs; 3 An Application: Linear Resolution and Prolog; 3.1 OSL-Resolution; 3.2 Horn Logic; 3.3 Input Resolution and Prolog; Appendix A: The Induction Principle.…”
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Architecture-aware optimization strategies in real-time image processing by Li, Chao, Balla-Arabe, Souleymane, Yang, Fan

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Robust and error-free geometric computing by Eberly, Dave

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Big data in medical science and healthcare management : diagnosis, therapy, side effects

Table of Contents: “…Autopilot and "doctor algorithm"? -- 1. Intro big data for healthcare? / Peter Langkafel -- 2. …”
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Real-time database systems : architecture and techniques

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The Fifth Decade of Cardiac Pacing. by Barold, S. Serge

Table of Contents: “…; 11 Permanent and Temporary Single-Lead VDD and DDD Pacing: State of the Art; 12 Pacemaker Memory: Basic Concepts and New Technology; 13 Future Trends in Pacemaker Technology; 14 Diagnosis of Supraventricular Tachyarrhythmias by Automatic Mode-Switching Algorithms of Dual-Chamber Pace.…”
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Features and processing in agreement by Mancini, Simona, 1978-

Table of Contents: “…1st/2nd vs. 3rd person: Person underspecification and context-dependencePronoun representation and interpretive anchors; The featural makeup of pronouns; Summary; Chapter Five; When disagreement is grammatical: Unagreement; Unagreement processing and the role of interpretive anchors; Unagreeing, null and overt subjects; Summary; Chapter Six; From feature bundles to feature an; Representations, algorithms and neuroanatomical bases of agreement; Relation to existing sentence comprehension models; Conclusion; Notes; Bibliography; Index…”
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Regression Analysis : Theory, Methods, and Applications by Sen, Ashish

Table of Contents: “…Random Variables -- B.1.2 Correlated Random Variables -- B.1.3 Sample Statistics -- B.1.4 Linear Combinations of Random Variables -- B.2 Random Vectors -- B.3 The Multivariate Normal Distribution -- B.4 The Chi-Square Distributions -- B.5 The F and t Distributions -- B.6 Jacobian of Transformations -- B.7 Multiple Correlation -- Problems -- C Nonlinear Least Squares -- C.1 Gauss-Newton Type Algorithms -- C.1.1 The Gauss-Newton Procedure -- C.1.2 Step Halving -- C.1.3 Starting Values and Derivatives -- C.1.4 Marquardt Procedure -- C.2 Some Other Algorithms -- C.2.1 Steepest Descent Method -- C.2.2 Quasi-Newton Algorithms -- C.2.3 The Simplex Method -- C.2.4 Weighting -- C.3 Pitfalls -- C.4 Bias, Confidence Regions and Measures of Fit -- C.5 Examples -- Problems -- Tables -- References -- Author Index.…”
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Machine Learning for Asset Management New Developments and Financial Applications

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Hands-On Reinforcement Learning with Python : Master Reinforcement and Deep Reinforcement Learning Using OpenAI Gym and TensorFlow. by Ravichandiran, Sudharsan

Table of Contents: “…Solving the taxi problem using Q learningSARSA; Solving the taxi problem using SARSA; The difference between Q learning and SARSA; Summary; Questions; Further reading; Chapter 6: Multi-Armed Bandit Problem; The MAB problem; The epsilon-greedy policy; The softmax exploration algorithm; The upper confidence bound algorithm; The Thompson sampling algorithm; Applications of MAB; Identifying the right advertisement banner using MAB; Contextual bandits; Summary; Questions; Further reading; Chapter 7: Deep Learning Fundamentals; Artificial neurons; ANNs; Input layer; Hidden layer; Output layer.…”
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The NURBS Book by Piegl, Les

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Hands-On Automated Machine Learning : a beginner's guide to building automated machine learning systems using AutoML and Python. by Das, Sibanjan

Table of Contents: “…; Why use AutoML and how does it help?; When do you automate ML?; What will you learn?; Core components of AutoML systems; Automated feature preprocessing; Automated algorithm selection; Hyperparameter optimization; Building prototype subsystems for each component; Putting it all together as an end-to-end AutoML system; Overview of AutoML libraries; Featuretools; Auto-sklearn; MLBox; TPOT; Summary.…”
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HTML5 Multimedia Development Cookbook. by Cruse, Dale

Table of Contents: “…More flexible footer contentApplying the outline algorithm; Getting ready; How to do it ... ; How it works ... ; There's more ... ; Are you sure?…”
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