Search Results - (((((((ant OR arts) OR kuang) OR wantsa) OR cantor) OR anne) OR warte) OR hints) algorithms.

Optimization advances in electric power systems

Table of Contents: “…Unreliability Costs -- 3.5. Proposed Algorithms -- 3.5.1. ES and TS Algorithms -- 3.5.2. …”
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Artificial intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques. by Deshpande, Anand

Table of Contents: “…Snowball stemming -- Lancaster stemming -- Lovins stemming -- Dawson stemming -- Lemmatization -- N-grams -- Feature extraction -- One hot encoding -- TF-IDF -- CountVectorizer -- Word2Vec -- CBOW -- Skip-Gram model -- Applying NLP techniques -- Text classification -- Introduction to Naive Bayes' algorithm -- Random Forest -- Naive Bayes' text classification code example -- Implementing sentiment analysis -- Frequently asked questions -- Summary -- Chapter 7: Fuzzy Systems -- Fuzzy logic fundamentals -- Fuzzy sets and membership functions -- Attributes and notations of crisp sets -- Operations on crisp sets -- Properties of crisp sets -- Fuzzification -- Defuzzification -- Defuzzification methods -- Fuzzy inference -- ANFIS network -- Adaptive network -- ANFIS architecture and hybrid learning algorithm -- Fuzzy C-means clustering -- NEFCLASS -- Frequently asked questions -- Summary -- Chapter 8: Genetic Programming -- Genetic algorithms structure -- KEEL framework -- Encog machine learning framework -- Encog development environment setup -- Encog API structure -- Introduction to the Weka framework -- Weka Explorer features -- Preprocess -- Classify -- Attribute search with genetic algorithms in Weka -- Frequently asked questions -- Summary -- Chapter 9: Swarm Intelligence -- Swarm intelligence -- Self-organization -- Stigmergy -- Division of labor -- Advantages of collective intelligent systems -- Design principles for developing SI systems -- The particle swarm optimization model -- PSO implementation considerations -- Ant colony optimization model -- MASON Library -- MASON Layered Architecture -- Opt4J library -- Applications in big data analytics -- Handling dynamical data -- Multi-objective optimization -- Frequently asked questions -- Summary -- Chapter 10: Reinforcement Learning -- Reinforcement learning algorithms concept.…”
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Pattern recognition by Theodoridis, Sergios, 1951-

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Debates in the digital humanities 2016

Table of Contents: “…The Differences Between Digital Humanities and Digital History / Stephen Robertson -- Digital History Perpetual Future Tense / Cameron Blevins -- Collections and/of Data: Art History and the Art Museum in the DH Mode / Matthew Battles and Michael Maizels -- Archaeology, the Digital Humanities, and the "Big Tent" / Ethan Watrall ; Navigating the Global Digital Humanities: Insights from Black Feminism / Roopika Risam. …”
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Machine Learning with Core ML : an IOS Developer's Guide to Implementing Machine Learning in Mobile Apps. by Newnham, Joshua

Table of Contents: “…Learning algorithms Auto insurance in Sweden; Supported learning algorithms; Considerations ; Summary; Chapter 3: Recognizing Objects in the World; Understanding images; Recognizing objects in the world; Capturing data ; Preprocessing the data; Performing inference ; Summary ; Chapter 4: Emotion Detection with CNNs; Facial expressions; Input data and preprocessing ; Bringing it all together; Summary ; Chapter 5: Locating Objects in the World; Object localization and object detection ; Converting Keras Tiny YOLO to Core ML; Making it easier to find photos; Optimizing with batches; Summary.…”
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Function Estimates. by Marron, J. S.

Table of Contents: “…Partial spline modelling of the tropopause and other discontinuitiesChoice of smoothing parameter in deconvolution problems -- Regression approximation using projections and isotropic kernels -- Will the art of smoothing ever become a science?…”
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Handbook of foot and ankle orthopedics by Shah, Rajiv

Table of Contents: “…-- Plantar heel pain -- Growing with flat feet: childhood to adulthood -- Lucid approach and simplistic management of diabetic foot -- Common foot and ankle infections: diagnosis and management -- Art of arthrodesis in foot and ankle -- Dealing with tendo achilles problems! …”
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Advances in time series forecasting. Volume 2

Table of Contents: “…INTRODUCTION -- CLASSICAL TIME SERIES FORECASTING MODELS -- ARTIFICIAL NEURAL NETWORKS FOR FORECASTING TIME SERIES -- A NEW ARTIFICIAL NEURAL NETWORK WITH DETERMINISTIC COMPONENTS -- APPLICATIONS -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A Fuzzy Time Series Approach Based on Genetic Algorithm with Single Analysis Process -- Ozge Cagcag Yolcu* -- INTRODUCTION -- FUZZY TIME SERIES -- RELATED METHODS -- Genetic Algorithm (GA) -- Single Multiplicative Neuron Model -- PROPOSED METHOD -- APPLICATIONS -- CONCLUSION AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Forecasting Stock Exchanges with Fuzzy Time Series Approach Based on Markov Chain Transition Matrix -- Cagdas Hakan Aladag1,* and Hilal Guney2 -- INTRODUCTION -- FUZZY TIME SERIES -- TSAUR 'S FUZZY TIME SERIES MARKOV CHAIN MODEL -- THE IMPLEMENTATION -- CONCLUSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- A New High Order Multivariate Fuzzy Time Series Forecasting Model -- Ufuk Yolcu* -- INTRODUCTION -- RELATED METHODOLOGY -- The Fuzzy C-Means (FCM) Clustering Method -- Single Multiplicative Neuron Model Artificial Neural Network (SMN-ANN) -- Fuzzy Time Series -- THE PROPOSED METHOD -- APPLICATIONS -- CONCLUSIONS AND DISCUSSION -- CONFLICT OF INTEREST -- ACKNOWLEDGEMENTS -- REFERENCES -- Fuzzy Functions Approach for Time Series Forecasting -- Ali Z. …”
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Ultrashort Pulse Laser Ablation of Bulk Materials Using Shaped Laser Beams. by Mikhaylov, Dmitriy

Table of Contents: “…Intro -- 1 Introduction -- 2 Theoretical principles and state of the art -- 2.1 Ultrashort pulse laser technology for materialprocessing -- 2.2 Laser beam shaping -- 2.3 Liquid crystal on silicon spatial light modulators -- 2.4 Phase retrieval algorithms -- 3 Materials and methods -- 3.1 Processed materials -- 3.2 System components -- 3.3 Characterization methods -- 4 Theoretical calculation of limits for theincrease of volumetric ablation rate -- 4.1 Heat accumulation modeling for spatially shapedbeams -- 4.2 Ideal requirements on laser sources as well as beamshaping and guiding optics…”
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Mastering SciPy. by Blanco-Silva, Francisco J.

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Proceedings of the 2009 International Conference on Software Technology and Engineering, Chennai, India, 24-26 July 2009

Table of Contents: “…Dananjayan -- Vertical handoff decision algorithm using system performance and user preference / R. …”
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PRINCIPLES OF QUANTUM ARTIFICIAL INTELLIGENCE. by Wichert, Andreas

Table of Contents: “…Computation; 2.1 Entscheidungsproblem; 2.1.1 Cantor's diagonal argument; 2.1.2 Reductio ad absurdum; 2.2 Complexity Theory; 2.2.1 Decision problems; 2.2.2 P and NP; 2.3 Church-Turing Thesis; 2.3.1 Church-Turing-Deutsch principle; 2.4 Computers; 2.4.1 Analog computers; 2.4.2 Digital computers; 2.4.3 Von Neumann architecture; 3. …”
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Pulmonary arterial hypertension : diagnosis and evidence-based treatment

Table of Contents: “…Combination therapy in pulmonary arterial hypertension / Anne Keogh and Marius Hoeper -- Interventional and surgical modalities of treatment for pulmonary arterial hypertension / Julio Sandoval and Ramona Doyle -- End points and clinical trial design in pulmonary arterial hypertension : clinical and regulatory perspectives / Andrew J. …”
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Graph Partitioning. by Bichot, Charles-Edmond

Table of Contents: “…Hendrickson-Leland coarsening algorithm; 2.3.4. The Heavy Edge Matching (HEM) algorithm; 2.4. …”
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Particle swarm optimisation : classical and quantum perspectives by Sun, Jun, 1971-

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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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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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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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