Search Results - (((((((ant OR kkantis) OR wants) OR hints) OR cantor) OR anne) OR fine) OR wkanting) algorithms.

Comorbidity in migraine

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Slantwise moves : games, literature, and social invention in nineteenth-century America by Guerra, Douglas

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Medical and Care Compunetics 2 : Medical and Care Compunetics 2. by Bos, L.

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Advances in digital technologies : proceedings of the 6th International Conference on Applications of Digital Information and Web Technologies 2015

Table of Contents: “…Application of Genetic Algorithms to Context-Sensitive Text MiningA Decision Tree Classification Model for Determining the Location for Solar Power Plant; A Framework for Multi-Label Learning Using Label Ranking and Correlation; A Comparative Analysis of Pruning Methods for C4.5 and Fuzzy C4.5; Subject Index; Author Index.…”
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Java Deep Learning Projects : Implement 10 Real-World Deep Learning Applications Using Deeplearning4j and Open Source APIs. by Karim, Rezaul

Table of Contents: “…; Artificial Neural Networks; Biological neurons; A brief history of ANNs; How does an ANN learn?; ANNs and the backpropagation algorithm; Forward and backward passes; Weights and biases; Weight optimization; Activation functions.…”
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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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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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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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Learning Geospatial Analysis with Python - Second Edition. by Lawhead, Joel

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Mastering machine learning with scikit-learn : learn to implement and evaluate machine learning solutions with scikit-learn by Hackeling, Gavin

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Learning OpenCV 3 Application Development. by Datta, Samyak

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Communicating process architectures 2002. by World Occam And Transputer User Group. Technical Meeting (25th: 2002: University Of Reading, U. K.)

Table of Contents: “…Acceptances, Behaviours and Infinite Activity in CSPP; HCSP: Imperative State and True Concurrency; Consolidating the Agreement Problem Protocol Verification Environment; On the Complexity of Buffer Allocation in Message Passing Systems; Java PastSet -- A Structured Distributed Shared Memory System; Synchronous Active Objects Introduce CSP's Primitives in Java; Configurable Collective Communication in LAM-MPI; Cache-Affinity Scheduling for Fine Grain Multithreading.…”
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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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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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Comprehensive Ruby Programming. by Hudgens, Jordan

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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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Pattern Recognition in Computational Molecular Biology : Techniques and Approaches by Elloumi, Mourad, Iliopoulos, Costas, Wang, Jason T. L., Zomaya, Albert Y.

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Handbook of power systems. II

Table of Contents: “…Cover -- Handbook of Power Systems II13; -- Preface of Volume II13; -- Contents of Volume II13; -- Contents13; of Volume I -- Contributors13; -- Part I Transmission and Distribution Modeling -- Recent Developments in Optimal Power Flow Modeling Techniques -- 1 Introduction -- 2 Physical Network Representation -- 3 Operational Constraints -- 4 Tap-Changing and Regulating Transformers -- 5 FACTS Devices -- 6 OPF Objective Functions and Formulations -- 7 OPF Solution Techniques -- 8 Numerical Examples -- 9 Conclusion -- References -- Algorithms for Finding Optimal Flows in Dynamic Networks -- 1 Optimal Dynamic Network Flow Models and Power Industry -- 2 Minimum Cost Dynamic Single: Commodity Flow Problems and Algorithms for Their Solving -- 3 Minimum Cost Dynamic Multicommodity Flow Problems and Algorithms for Their Solving -- References -- Signal Processing for Improving Power Quality -- 1 Wavelet-based Algorithm for Harmonics Analysis -- 2 Wavelet-based Algorithm for Nonstationary Power System Waveform Analysis -- 3 Wavelet-GA-ANN Based Hybrid Model for Accurate Prediction of Short-term Load Forecast -- 4 Conclusions -- References -- Transmission Valuation Analysis based on Real Options with Price Spikes -- 1 Introduction -- 2 Behavior of Commodity Prices -- 3 Valuation of Obligations and Options -- 4 Valuation in the Presence of Spikes -- 5 Conclusions -- References -- Part II Forecasting in Energy -- Short-term Forecasting in Power Systems: A Guided Tour -- 1 Introduction -- 2 Electricity Load Forecasting -- 3 Wind Power Forecasting -- 4 Forecasting Electricity Prices -- 5 Conclusions -- References -- State-of-the-Art of Electricity Price Forecasting in a Grid Environment -- 1 Introduction -- 2 State-of-the-Art Techniques of Electricity Price Forecasting -- 3 Input8211;Output Specifications of Electricity Price Forecasting Techniques -- 4 Comparing Existing Statistical Techniques for Electricity Price Forecasting -- 5 Implementations of Electricity Price Forecasting in a Grid Environment -- 6 Conclusions -- References -- Modelling the Structure of Long-Term Electricity Forward Prices at Nord Pool -- 1 Introduction -- 2 Long-term Forward Price Process -- 3 Model Estimation -- 4 Conclusions -- References -- Hybrid Bottom-Up/Top-Down Modeling of Prices in Deregulated Wholesale Power Markets -- 1 Introduction -- 2 Top-Down Models for Electricity Price Forecasting -- 3 Hybrid Bottom-Up/Top-Down Modeling -- 4 A Hybrid Model for the New Zealand Electricity Market -- 5 A Hybrid Model for the Australian Electricity Market -- 6 Conclusions -- References -- Part III Energy Auctions and Markets -- Agent-based Modeling and Simulation of Competitive Wholesale Electricity Markets -- 1 Introduction -- 2 Agent-based Modeling and Simulation -- 3 Behavioral Modeling -- 4 Market Modeling -- 5 Conclusions -- References -- Futures Market Trading for Electricity Producers and Retailers -- 1 Introduction: Futures Market Trading -- 2 Producer Trading -- 3 Retailer Trading -- 4 Conclusions -- References -- A Decision Support System for Generation Planning and Operation in Electricity Markets -- 1 Introduction -- 2 Long-term Stochastic Market Planning Model -- 3 Medium-term Stochastic Hydrothermal Coordination Model -- 4 Medium-term Stochastic Simulation Model -- T$29828.…”
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Craniofacial Identification. by Wilkinson, Caroline, 1965-

Table of Contents: “…3.2 Mathematical model of facial appearance3.2.1 Training -- the generation of the facial appearance model; 3.2.2 Decomposition of a face into appearance parameters; 3.2.3 Synthesis of a face from its appearance parameters; 3.2.4 Summary of appearance model properties; 3.2.5 Significance of appearance model representation; 3.2.6 Stochastic search algorithm; 3.3 Systematic operations in the EFIT-V system; 3.3.1 Locking facial features; 3.3.2 Feature clone tool; 3.3.3 Blend tool; 3.3.4 Facial attribute manipulation; 3.3.5 Local feature manipulation; 3.3.6 Applying fine details to a composite.…”
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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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