Search Results - (((((((ant OR alter) OR find) OR iskantor) OR cantor) OR anne) OR salted) OR wanting) algorithms.

Ieee series on digital & mobile communication : fundamentals of convolutional coding (2nd edition). by Johannesson, Rolf; Zigangirov, Kamil Sh

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Critical Properties of Phi4- Theories. by Schulte-Frohlinde, Verena

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Handbook of Mathematical Induction. by Gunderson, David S.

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Global optimization methods in geophysical inversion by Sen, Mrinal K.

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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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Pattern recognition in industry by Bhagat, Phiroz

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Clinical Atlas of Canine and Feline Dermatology by Coyner, Kimberly S.

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Atlas of ambulatory EEG

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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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Entrepreneurship in technology for ASEAN

Table of Contents: “…2.1.1 Environmental Studies2.1.2 Economic Studies; 2.1.3 Social Causes; 2.1.4 Health Causes; 2.2 Spatial Data Analysis Approaches; 2.2.1 CURE Algorithm; 2.2.2 CHAMELEON Algorithm; 2.2.3 DBSCAN Algorithm; 2.2.4 k-Means Algorithm; 2.2.5 RANKPRO; 2.2.6 STING; 2.2.7 Wave Cluster; 2.3 Findings from Literature Review; 3 Methodology; 3.1 Data Description; 4 Theory and Calculation; 5 Results; 6 Discussion; 7 Conclusion; References; 4 Factors Influencing Implementation of Lean Manufacturing: Case on Manufacturing in Indonesia; Abstract; 1 Introduction; 2 Literature Review; 2.1 Lean Manufacturing.…”
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Statistical methods of geophysical data processing by Troi︠a︡n, V. N. (Vladimir Nikolaevich)

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Anesthesia student survival guide : a case-based approach

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Advanced data structures : theory and applications by Saha, Suman, Shukla, S. K. (Shailendra Kumar), 1969-

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Mastering predictive analytics with R : machine learning techniques for advanced models by Miller, James D.

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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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Maternal critical care : a multidisciplinary approach

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Data mining techniques : for marketing, sales, and customer relationship management by Linoff, Gordon, Berry, Michael J. A.

Table of Contents: “…-- Data mining applications in marketing and customer relationship management -- The data mining process -- Statistics 101: What you should know about data -- Descriptions and prediction: profiling and predictive modeling -- Data mining using classic statistical techniques -- Decision trees -- Artificail neural networks -- Nearest neighbor approaches: Memory-based reasoning and collaborative filtering -- Knowing when to worry: Using survival analysis to understand customers -- Genetic algorithms and swarm intelligence -- Tell me something new: Pattern discovery and data mining -- Finding islands of similarity: Automatic cluster detection -- Alternative approaches to cluster detection -- Market basket analysis and association rules -- Link analysis -- Data warehousing, OLAP, analytic sandboxes, and data mining -- Building customer signatures -- Derived variables: Making the data mean more -- Too much of a good thing? …”
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Modeling and optimization of air traffic by Delahaye, Daniel

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Mathematical modeling with multidisciplinary applications

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Reinforcement Learning with TensorFlow : a beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym. by DUTTA, SAYON

Table of Contents: “…Markov decision processesThe Markov property; The S state set; Actions; Transition model; Rewards; Policy; The sequence of rewards -- assumptions; The infinite horizons; Utility of sequences; The Bellman equations; Solving the Bellman equation to find policies; An example of value iteration using the Bellman equation; Policy iteration; Partially observable Markov decision processes; State estimation; Value iteration in POMDPs; Training the FrozenLake-v0 environment using MDP; Summary; Chapter 4: Policy Gradients; The policy optimization method; Why policy optimization methods?…”
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