Search Results - (((((((want OR find) OR semantic) OR carter) OR cantor) OR anne) OR maarten) OR mantis) algorithms.

Language engineering for lesser-studied languages

Table of Contents: “…Derzhanski -- Computational morphology for lesser-studied languages / Kemal Olfazer -- Practical syntastic processing of flexible word order languages with dynamic syntax / David Tugwell -- Computation field semantics : acquiring an ontological-semantic lexicon for a new language / Sergei Nirenburg and Marjorie McShane -- Applying machine translation for low- and middle-density languages / Stella Markantonatou [and others] -- Language resources for semitic languages : challenges and solutions / Shuly Wintner -- Low-density language strategies for Persian and Armenian / Karine Megerdoomian.…”
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Theory of information : fundamentality, diversity and unification by Burgin, M. S. (Mark Semenovich)

Table of Contents: “…Algorithmic information theory based on recursive algorithms : recursive approach. 5.3. …”
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Digital signal processing with kernel methods by Rojo-Álvarez, José Luis, 1972-, Martínez-Ramón, Manel, 1968-, Muñoz Marí, Jordi, Camps-Valls, Gustavo, 1972-

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Image processing for embedded devices : from CFA data to image/video coding

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Media technologies : essays on communication, materiality, and society

Table of Contents: “…Bowker -- "What Do We Want?" "Materiality!" "When Do We Want It?" "Now!" …”
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Agent-based computing

Table of Contents: “…Analysis of Algorithm -- 2.1.1. Chain-Like Agent Structure -- 2.1.2. …”
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Tales of Literacy for the 21st Century : the Literary Agenda. by Wolf, Maryanne

Table of Contents: “…Finding the nameMeanings-connecting semantic and syntactic systems; Semantic contributions to the meaning of a word; Syntactic contributions to understanding the word; Notes; 5: The Deep Reading Brain; Episteme-connecting the name to the reader's knowledge; Entry processes-imagery, perspective-taking, and background knowledge; Imagery; Perspective-taking; Background knowledge; Metacognitive "scientific method" processes-analogical, inferential, and critical analytical abilities; Analogy as bridge; Inferential abilities (observation, deduction, and induction); Critical analyses.…”
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Gradience in grammar : generative perspectives

Table of Contents: “…Cohn -- Gradedness : interpretive dependencies and beyond / Eric Reuland -- Linguistic and metalinguistic tasks in phonology : methods and findings / Stefan A. Frisch and Adrienne Stearns -- Intermediate syntactic variants in a dialect : standard speech repertoire and relative acceptability / Leonie Cornips -- Gradedness and optionality in mature and developing grammars / Antonella Sorace -- Decomposing gradience : quantitative versus qualitative distinctions / Matthias Schlesewsky, Ina Bornkessel, and Brian McElree -- Gradient perception of intonation / Caroline Féry and Ruben Stoel -- Prototypicality judgements as inverted perception / Paul Boersma -- Modelling productivity with the gradual learning algorithm : the problem of accidentally exceptionless generalizations / Adam Albright and Bruce Hayes -- Gradedness as relative efficiency in the processing of syntax and semantics / John A. …”
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Calculating the Secrets of Life : Contributions of the Mathematical Sciences to Molecular Biology. by Lander, Eric S.

Table of Contents: “…Efficient AlgorithmsStatistical Significance; Excursion: Susceptibility to Colon Cancer in Mice and the Large Deviation Theory of Diffusion Processes; PHYSICAL MAPPING; Assembling Physical Maps by ""Fingerprinting"" Random Clones; Excursion: Designing a Strategy to Map the Human Genome; CONCLUSION; REFERENCES; Chapter 3 Seeing Conserved Signals: Using Algorithms to Detect Similarities between Biosequences ; FINDING GLOBAL SIMILARITIES; Visualizing Alignments: Edit Graphs; The Basic Dynamic Programming Algorithm; FINDING LOCAL SIMILARITIES; VARIATIONS ON SEQUENCE COMPARISON.…”
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Emitter detection and geolocation for electronic warfare by O'Donoughue, Nicholas A.

Table of Contents: “…6.2 MAXIMUM LIKELIHOOD ESTIMATION -- 6.3 OTHER ESTIMATORS -- 6.3.1 Minimum Variance Unbiased Estimators -- 6.3.2 Bayes Estimators -- 6.3.3 Least Square Estimators -- 6.3.4 Convex Estimators -- 6.3.5 Tracking Estimators -- 6.4 PERFORMANCE MEASURES -- 6.4.1 Root Mean Squared Error (RMSE) -- 6.4.2 CRLB -- 6.4.3 Angle Error Variance and Confidence Intervals -- 6.5 PROBLEM SET -- References -- Chapter 7 Direction-Finding Systems -- 7.1 BEAM PATTERN-BASED DIRECTION FINDING -- 7.1.1 Implementation -- 7.1.2 Performance -- 7.2 WATSON-WATT DIRECTION FINDING -- 7.2.1 Implementation -- 7.2.2 Performance…”
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Quantum Computation And Quantum Information Theory : Reprint Volume with Introductory Notes for ISI TMR Network School. by Zeilinger, A.

Table of Contents: “…2.4 Going Beyond Bell's Theorem 2.5 What is Wrong with These Elements of Reality -- 2.6 Observation of Three-Photon Greenberger-Horne-Zeilinger Entanglement -- Quantum Algorithms -- 3. Quantum Algorithms -- 3.1 An Overview of Quantum Computing -- 3.2 On Quantum Algorithms…”
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Domain Decomposition Methods in Science and Engineering : Proceedings of the Seventh International Conference on Domain Decomposition October 27-30, 1993, the Pennsylvania State U... by Keyes, David E.

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Foundations of bilevel programming by Dempe, Stephan

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HTML5 Multimedia Development Cookbook. by Cruse, Dale

Table of Contents: “…; How it works ... ; There's more ... ; Validation as an aid, not a crutch; Eric Meyer's funny; Where to find validators; See also.…”
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Future Farming. by Shukla, Praveen Kumar

Table of Contents: “…PROPOSED METHODOLOGY -- Pre-processing -- Leaf Image from Plants -- Segmentation Model Using Improved Canny Algorithm -- Steps of Improved Canny Algorithm -- Leaf Image Feature Selection Using Hybrid Black Widow Optimization Algorithm with Mayfly Optimization Algorithm (BWO-MA) -- Pseudo-Code of the Hybrid (BWO-MA) Algorithm -- Output: Objective Function's -RMSE -- Leaf Image Classification Using (BWO-MA) with ANN -- Hyper-Parameter Tuning With (BWO-MA) -- RESULT AND DISCUSSION -- Dataset Description -- Evaluation & Results -- CONCLUSION -- REFERENCES…”
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Rough fuzzy image analysis : foundations and methodologies

Table of Contents: “…1. Cantor, fuzzy, near, and rough sets in image analysis / James F. …”
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Handbook of Modal Logic. by Blackburn, Patrick

Table of Contents: “…13 Quantified Modal Logic13.1 Syntax and Semantics; 13.2 Constant Domain Tableaus; 13.3 Soundness and Completeness; 13.4 Variations; 14 Conclusion; Bibliography; Chapter 3 Complexity of Modal Logic; 1 Introduction; 1.1 Examples of decision problems in modal logic; 1.2 A simple and a hard problem; 1.3 The model checking problem; 1.4 The consequence problem; 1.5 A tiling logic; 2 Decision algorithms; 2.1 Selection of points; 2.2 Filtration; 2.3 Hintikka set elimination; 2.4 Hintikka set elimination without constraints; 2.5 Forcing exponentially deep paths; 2.6 Tree automata; 2.7 Pseudo-models.…”
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Ivor Horton's beginning Visual C++ 2012 by Horton, Ivor

Table of Contents: “…Finding the Length of a Null-Terminated String --…”
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Building Machine Learning Systems with Python. by Richert, Willi

Table of Contents: “…A more complex dataset and a more complex classifierLearning about the Seeds dataset; Features and feature engineering; Nearest neighbor classification; Binary and multiclass classification; Summary; Chapter 3: Clustering -- Finding Related Posts; Measuring the relatedness of posts; How not to do it; How to do it; Preprocessing -- similarity measured as similar number of common words; Converting raw text into a bag-of-words; Counting words; Normalizing the word count vectors; Removing less important words; Stemming; Installing and using NLTK; Extending the vectorizer with NLTK's stemmer.…”
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Enterprise artificial intelligence transformation : a playbook for the next generation of business and technology leaders by Haq, Rashed

Table of Contents: “…Types of Uses for Machine Learning -- Types of Machine Learning Algorithms -- Supervised, Unsupervised, and Semisupervised Learning -- Making Data More Useful -- Semantic Reasoning -- Applications of AI -- PART II Artificial Intelligence in the Enterprise -- Chapter 3 AI in E-Commerce and Retail -- Digital Advertising -- Marketing and Customer Acquisition -- Cross-Selling, Up-Selling, and Loyalty -- Business-to-Business Customer Intelligence -- Dynamic Pricing and Supply Chain Optimization -- Digital Assistants and Customer Engagement -- Chapter 4 AI in Financial Services -- Anti-Money Laundering.…”
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