Search Results - (((((((ant OR manthe) OR mind) OR semantic) OR cantor) OR anne) OR halten) OR ranting) algorithms.

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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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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Modern optimization methods for science, engineering and technology

Table of Contents: “…Implementing the traveling salesman problem using a modified ant colony optimization algorithm -- 5.1. ACO and candidate list -- 5.2. …”
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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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Learning Boost C++ libraries : solve practical programming problems using powerful, portable, and expressive libraries from Boost by Mukherjee, Arindam

Table of Contents: “…Shared ownership semanticsboost::shared_ptr and std::shared_ptr; Intrusive smart pointers -- boost::intrusive_ptr; shared_array; Managing non-memory resources using smart pointers; Self-test questions; Summary; References; Chapter 4: Working with Strings; Text processing with Boost String Algorithms library; Using Boost String Algorithms; Find algorithms; Case-conversion and trimming algorithms; The replace and erase algorithms; The split and join algorithms; Splitting text using the Boost Tokenizer library; Tokenizing based on separators…”
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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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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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Language and logos : studies in theoretical and computational linguistics

Table of Contents: “…CONTENTS -- Preface -- Part I Syntax -- A Spurious Genitive Puzzle in Polish -- Semantic Type Effects on Crossing Movement in German -- Me and Chomsky -- On the Typology of Verb Second -- Movement from Verb-Second Clauses Revisited -- Spurious Ambiguities and the Parentheticals Debate -- Part II Semantics -- On Squeamishness of the Royal Kind -- Information Structure of schon -- When- Clauses, Factive Verbs and Correlates -- The Proof Theory of Partial Variables -- Brentano's Apple -- How to Interpret Expletive Negation under bevor in German…”
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Analysis of biological data : a soft computing approach

Table of Contents: “…Feature selection for cancer classification using ant colony optimization and support vector machines / A. …”
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Advanced Methods and Technologies for Agent and Multi-Agent Systems. by Edited by

Table of Contents: “…""Role and Member Selection in Team Formation Using Resource Estimation""""Incorporating Explicit Coordination Mechanisms by Agents to Obtain Green Waves""; ""Communication Leading to Coalition Nash Equilibrium I""; ""Agent-Based Optimization""; ""An Agent-Based Cooperative Population Learning Algorithm for Vehicle Routing Problem with Time Windows""; ""Mobile Agent-based Dynamic Resource Allocation Method for Query Optimization in Data Grid Systems""; ""Memetic Multi-Agent Computing in Difficult Continuous Optimisation""; ""Web Services and Semantic Web""…”
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Visual Data Mining : The VisMiner Approach. by Anderson, Russell K.

Table of Contents: “…Regression Analysis -- The Regression Model -- Correlation and Causation -- Algorithms for Regression Analysis -- Assessing Regression Model Performance -- Model Validity -- Looking Beyond R2 -- Polynomial Regression -- Artificial Neural Networks for Regression Analysis -- Dataset Preparation -- Tutorial -- A Regression Model for Home Appraisal -- Modeling with the Right Set of Observations -- Exercise 6.1 -- ANN Modeling -- The Advantage of ANN Regression -- Top-Down Attribute Selection -- Issues in Model Interpretation -- Model Validation -- Model Application -- Summary…”
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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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Process Control : a Practical Approach. by King, Michael

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Focus on artificial neural networks

Table of Contents: “…ARTIFICIAL NEURAL NETWORKS (ANNS) -- 3. MICROEMULSIONS -- 4. APPLICATION OF ANNS IN THE DEVELOPMENT OF MICROEMULSION DRUG DELIVERY SYSTEMS -- 4.1. …”
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Implementing cryptography using Python by Bray, Shannon, 1972-

Table of Contents: “…Chapter 2 Cryptographic Protocols and Perfect Secrecy -- The Study of Cryptology -- Understanding Cryptography -- Cryptography's Famous Family: Alice and Bob -- Diffie-Hellman -- Data Origin Authentication -- Entity Authentication -- Symmetric Algorithms -- Asymmetric Algorithms -- The Needham-Schroeder Protocols -- The Otway-Rees Protocol -- Kerberos -- Multiple-Domain Kerberos -- X.509 -- Formal Validation of Cryptographic Protocols -- Configuring Your First Cryptographic Library -- Understanding Cryptanalysis -- Brute-Force Attacks -- Side-Channel Attacks -- Social Engineering…”
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Metaheuristics for vehicle routing problems. by Labadie, Nacima

Table of Contents: “…Memetic algorithm; 4.1.3. Memetic algorithm with population management; 4.1.4. …”
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Industrial control systems

Table of Contents: “…EXTRACTIVE FERMENTATION PROCESS FOR BIOETHANOL PRODUCTION ; 3. PLANT MODEL BASED ON ANN ; 3.1. ANN Configurations.…”
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The Decisionist Imagination : Sovereignty, Social Science and Democracy in the 20th Century. by Guilhot, Nicolas

Table of Contents: “…Reading the International Mind; Chapter 2. Militant Democracy as Decisionist Liberalism; Chapter 3. …”
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Kernels for structured data by Gärtner, Thomas

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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: “…A Communicating Threads (CT) Case Study: JIWYPrioritised Dynamic Communicating Processes -- Part I; Prioritised Dynamic Communicating Processes -- Part II; Implementing a Distributed Algorithm for Detection of Local Knots and Cycles in Directed Graphs; Author Index.…”
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