Search Results - (((((((kent OR arts) OR wikant) OR data) OR cantor) OR anne) OR walted) OR canton) algorithms.

How to think about algorithms by Edmonds, Jeff, 1963-

Table of Contents: “…Iterative algorithms: measures of progress and loop invariants -- Examples using more-of-the-input loop invariants -- Abstract data types -- Narrowing the search space: binary search -- Iterative sorting algorithms -- Euclid's GCD algorithm -- The loop invariant for lower bounds -- Abstractions, techniques, and theory -- Some simple examples of recursive algorithms -- Recursion on trees -- Recursive images -- Parsing with context-free grammars -- Definition of optimization problems -- Graph search algorithms -- Network flows and linear programming -- Greedy algorithms -- Recursive backtracking -- Dynamic programming algorithms -- Examples of dynamic programs -- Reductions and NP-completeness -- Randomized algorithms -- Existential and universal quantifiers -- Time complexity -- Logarithms and exponentials -- Asymptotic growth -- Adding-made-easy approximations -- Recurrence relations -- A formal proof of correctness.…”
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Electronic and Algorithmic Trading Technology : the Complete Guide. by Kim, Kendall

Table of Contents: “…Front Cover; Electronic and Algorithmic Trading Technology; Copyright Page; Dedication Page; Contents; About the Author; Series Preface; Introduction; Chapter 1: Overview of Electronic and Algorithmic Trading; Chapter 2: Automating Trade and Order Flow; Chapter 3: The Growth of Program and Algorithmic Trading; Chapter 4: Alternative Execution Venues; Chapter 5: Algorithmic Strategies; Chapter 6: Algorithmic Feasibility and Limitations; Cahpter 7: Electronic Trading Networks; Chapter 8: Effective Data Management; Chapter 9: Minimizing Execution Costs; Chapter 10: Transaction Cost Research.…”
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Graph Algorithms and Applications 2 : Solid-Electrolyte Interphase. by Liotta, Giuseppe

Table of Contents: “…Difference Metrics for Interactive Orthogonal Graph Drawing Algorithms Techniques for the Refinement of Orthogonal Graph Drawings ; A Split & Push Approach to 3D Orthogonal Drawing ; Using Graph Layout to Visualize Train Interconnection Data.…”
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Graph algorithms and applications 4

Table of Contents: “…Special issue on selected papers from the Seventh International Workshop on Algorithms and Data Structures, WADS 2001 / Guest editor(s): Giuseppe Liotta and Ioannis G. …”
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Evolutionary computation in gene regulatory network research

Table of Contents: “…I: Preliminaries -- A Brief Introduction to Evolutionary and other Nature-Inspired Algorithms / Nasimul Noman and Hitoshi Iba -- Mathematical Models and Computational Methods for Inference of Genetic Networks / Tatsuya Akutsu -- Gene Regulatory Networks: Real Data Sources and Their Analysis / Yuji Zhang -- II: EAs for Gene Expression Data Analysis and GRN Reconstruction -- Biclustering Analysis of Gene Expression Data Using Evolutionary Algorithms / Alan Wee-Chung Liew -- Inference of Vohradský's Models of Genetic Networks Using a Real-Coded Genetic Algorithm / Shuhei Kimura -- GPU-Powered Evolutionary Design of Mass-Action-Based Models of Gene Regulation / Marco S. …”
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Analysis of biological networks

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Optimization of Computer Networks : Modeling and Algorithms: a Hands-On Approach. by Pavón Mariño, Pablo

Table of Contents: “…Chapter 9: Primal Gradient Algorithms -- 9.1 Introduction -- 9.2 Penalty Methods -- 9.3 Adaptive Bifurcated Routing -- 9.4 Congestion Control using Barrier Functions -- 9.5 Persistence Probability Adjustment in MAC Protocols -- 9.6 Transmission Power Assignment in Wireless Networks -- 9.7 Notes and Sources -- 9.8 Exercises -- References -- Chapter 10: Dual Gradient Algorithms -- 10.1 Introduction -- 10.2 Adaptive Routing in Data Networks -- 10.3 Backpressure (Center-Free) Routing -- 10.4 Congestion Control -- 10.5 Decentralized Optimization of CSMA Window Sizes -- 10.6 Notes and Sources…”
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Metaheuristic optimization for the design of automatic control laws by Sandou, Guillaume

Table of Contents: “…Motivations to use metaheuristic algorithms -- 1.3. Organization of the book -- Chapter 2. …”
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Digital Video and HD : Algorithms and Interfaces. by Poynton, Charles

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Network Routing : Algorithms, Protocols, and Architectures. by Medhi, Deep

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Practical Algorithms for 3D Computer Graphics, Second Edition. by Ferguson, R. Stuart (Robin Stuart), 1953-

Table of Contents: “…Basic theory and mathematical results; 3. Data structures for 3D graphics; 4. Basic visualization; 5. …”
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Digital is destroying everything : what the tech giants won't tell you about how robots, big data, and algorithms are radically remaking your future / by Edwards, Andrew V., 1956-

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The data imperative : how digitalization is reshaping management, organizing, and work by Schildt, Henri

Table of Contents: “…Cover -- The Data Imperative: How Digitalization is Reshaping Management, Organizing, and Work -- Copyright -- Acknowledgements -- Contents -- List of Figures -- List of Tables -- List of Boxes -- Chapter 1: Digital Transformation -- From Business Computing to the Digital Transformation -- Ubiquity and the Value of Digital Data in Business -- From Human Routines to Algorithmic Processing -- Digitalization as a Change in the Mindset and Norms of Management -- Data as a Management Priority -- Outline of the Chapters -- Digital Transformation and the Future of Work…”
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Data Mining : Practical Machine Learning Tools and Techniques, Second Edition. by Witten, I. H. (Ian H.)

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Data mining : practical machine learning tools and techniques by Witten, I. H. (Ian H.), Frank, Eibe, Hall, Mark A. (Mark Andrew)

Table of Contents: “…-- Input : concepts, instances, and attributes -- Output : knowledge representation -- Algorithms : the basic methods -- Credibility : evaluating what's been learned -- Implementations : real machine learning schemes -- Data transformation -- Ensemble learning -- Moving on : applications and beyond -- Introduction to Weka -- The explorer -- The knowledge flow interface -- The experimenter -- The command-line interface -- Embedded machine learning -- Writing new learning schemes -- Tutorial exercises for the weka explorer.…”
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Data Love : the Seduction and Betrayal of Digital Technologies. by Simanowski, Roberto

Table of Contents: “…Data-Mining Business; 7. Social Engineers Without a Cause; 8. …”
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Data Mining : a Tutorial-Based Primer, Second Edition. by Roiger, Richard J.

Table of Contents: “…3.2 A BASIC COVERING RULE ALGORITHM3.3 GENERATING ASSOCIATION RULES; 3.3.1 Confidence and Support; 3.3.2 Mining Association Rules: An Example; 3.3.3 General Considerations; 3.4 THE K-MEANS ALGORITHM; 3.4.1 An Example Using K-means; 3.4.2 General Considerations; 3.5 GENETIC LEARNING; 3.5.1 Genetic Algorithms and Supervised Learning; 3.5.2 General Considerations; 3.6 CHOOSING A DATA MINING TECHNIQUE; 3.7 CHAPTER SUMMARY; 3.8 KEY TERMS; Section II: Tools for Knowledge Discovery; Chapter 4. …”
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Data Mining.

Table of Contents: “…-- Decision Tree Induction -- GINI Index -- Entropy -- Misclassification Measure -- Practical Issues Regarding Decision Trees -- Predictive Accuracy -- STOP Condition for Split -- Pruning Decision Trees -- Extracting Classification Rules from Decision Trees -- Advantages of Decision Trees -- Data Mining Techniques and Models -- Data Mining Methods -- Bayesian Classifier -- Artificial Neural Networks -- Perceptron -- Types of Artificial Neural Networks -- Probabilistic Neural Networks -- Some Neural Networks Applications -- Support Vector Machines -- Association Rule Mining -- Rule-Based Classification -- k-Nearest Neighbor -- Rough Sets -- Clustering -- Hierarchical Clustering -- Non-hierarchical/Partitional Clustering -- Genetic Algorithms -- Components of GAs -- Architecture of GAs -- Applications -- Classification Performance Evaluation -- Costs and Classification Accuracy -- ROC (Receiver Operating Characteristic) Curve -- Statistical Methods for Comparing Classifiers -- References -- Index.…”
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Data Science : The Executive Summary - a Technical Book for Non-Technical Professionals. by Cady, Field

Table of Contents: “…2.1.5 What Data Scientists Don't (Necessarily) Do -- 2.1.5.1 Working Without Data -- 2.1.5.2 Working with Data that Can't Be Interpreted -- 2.1.5.3 Replacing Subject Matter Experts -- 2.1.5.4 Designing Mathematical Algorithms -- 2.2 Data Science in an Organization -- 2.2.1 Types of Value Added -- 2.2.1.1 Business Insights -- 2.2.1.2 Intelligent Products -- 2.2.1.3 Building Analytics Frameworks -- 2.2.1.4 Offline Batch Analytics -- 2.2.2 One-Person Shops and Data Science Teams -- 2.2.3 Related Job Roles -- 2.2.3.1 Data Engineer -- 2.2.3.2 Data Analyst -- 2.2.3.3 Software Engineer…”
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Big data : concepts, technology and architecture by Balusamy, Balamurugan, R, Nandhini Abirami, Kadry, Seifedine, 1977-, Gandomi, Amir Hossein

Table of Contents: “…143</p> <p>5.2.3HDFS Architecture. 143</p> <p>5.2.4HDFS Read/Write Operation. 146</p> <p>5.2.5Rack Awareness. 148</p> <p>5.2.6Features of HDFS. 149</p> <p>5.2.6.1Cost-effective. 149</p> <p>5.2.6.2Distributed storage. 149</p> <p>5.2.6.3Data Replication. 149</p> <p>5.3 Hadoop Computation. 149</p> <p>5.3.1MapReduce. 149</p> <p>5.3.1.1Mapper. 151</p> <p>5.3.1.2Combiner. 151</p> <p>5.3.1.3 Reducer. 152</p> <p>5.3.1.4 JobTracker and TaskTracker. 153</p> <p>5.3.2 MapReduce Input Formats. 154</p> <p>5.3.3 MapReduce Example. 156</p> <p>5.3.4 MapReduce Processing. 157</p> <p>5.3.5 MapReduce Algorithm.. 160</p> <p>5.3.6 Limitations of MapReduce. 161</p> <p>5.4Hadoop 2.0. 161</p> <p>5.4.1Hadoop 1.0 limitations. 162</p> <p>5.4.2 Features of Hadoop 2.0. 163</p> <p>5.4.3 Yet Another Resource Negotiator (YARN). 164</p> <p>5.4.3 Core components of YARN.. 165</p> <p>5.4.3.1 ResourceManager. 165</p> <p>5.4.3.2 NodeManager. 166</p> <p>5.4.4 YARN Scheduler. 169</p> <p>5.4.4.1 <i>FIFO scheduler</i>. 169</p> <p>5.4.4.2 <i>Capacity Scheduler</i>. 170</p> <p>5.4.4.3 <i>Fair Scheduler</i>. 170</p> <p>5.4.5 Failures in YARN.. 171</p> <p>5.4.5.1ResourceManager failure. 171</p> <p>5.4.5.2 ApplicationMaster failure. 172</p> <p>5.4.5.3 NodeManagerFailure. 172</p> <p>5.4.5.4 Container Failure. 172</p> <p>5.3 HBASE. 173</p> <p>5.4 Apache Cassandra. 176</p> <p>5.5 SQOOP. 177</p> <p>5.6 Flume. 179</p> <p>5.6.1 Flume Architecture. 179</p> <p>5.6.1.1 Event. 180</p> <p>5.6.1.2 Agent. 180</p> <p>5.7 Apache Avro. 181</p> <p>5.8 Apache Pig. 182</p> <p>5.9 Apache Mahout. 183</p> <p>5.10 Apache Oozie. 183</p> <p>5.10.1 Oozie Workflow.. 184</p> <p>5.10.2 Oozie Coordinators. 186</p> <p>5.10.3 Oozie Bundles. 187</p> <p>5.11 Apache Hive. 187</p> <p>5.11 Apache Hive. 187</p> <p>Hive Architecture. 189</p> <p>Hadoop Distributions. 190</p> <p>Chapter 5refresher. 191</p> <p>Conceptual short questions with answers. 194</p> <p>Frequently asked Interview Questions. 199</p> <p>Chapter Objective. 200</p> <p>6.1 Terminologies of Big Data Analytics. 201</p> <p><i>Data Warehouse</i>. 201</p> <p><i>Business Intelligence</i>. 201</p> <p><i>Analytics</i>. 202</p> <p>6.2 Big Data Analytics. 202</p> <p>6.2.1 Descriptive Analytics. 204</p> <p>6.2.2 Diagnostic Analytics. 205</p> <p>6.2.3 Predictive Analytics. 205</p> <p>6.2.4 Prescriptive Analytics. 205</p> <p>6.3 Data Analytics Lifecycle. 207</p> <p>6.3.1 Business case evaluation and Identify the source data. 208</p> <p>6.3.2 Data preparation. 209</p> <p>6.3.3 Data Extraction and Transformation. 210</p> <p>6.3.4 Data Analysis and visualization. 211</p> <p>6.3.5 Analytics application. 212</p> <p>6.4 Big Data Analytics Techniques. 212</p> <p>6.4.1 Quantitative Analysis. 212</p> <p>6.4.3 Statistical analysis. 214</p> <p>6.4.3.1 A/B testing. 214</p> <p>6.4.3.2 Correlation. 215</p> <p>6.4.3.3 Regression. 218</p> <p>6.5 Semantic Analysis. 220</p> <p>6.5.1 Natural Language Processing. 220</p> <p>6.5.2 Text Analytics. 221</p> <p>6.7 Big Data Business Intelligence. 222</p> <p>6.7.1 Online Transaction Processing (OLTP). 223</p> <p>6.7.2 Online Analytical Processing (OLAP). 223</p> <p>6.7.3 Real-Time Analytics Platform (RTAP). 224</p> <p>6.6Big Data Real Time Analytics Processing. 225</p> <p>6.7 Enterprise Data Warehouse. 227</p> <p>Chapter 6 Refresher. 228</p> <p>Concept…”
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