Search Results - (((((((ant OR wkant) OR mwantis) OR when) OR cantor) OR anne) OR shane) OR hints) algorithms.

Discovering knowledge in data : an introduction to data mining by Larose, Daniel T.

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Literature and encyclopedism in Enlightenment Britain : the pursuit of complete knowledge by Rudy, Seth, 1978-

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Optimization of Observation and Control Processes. by Malyshev, V. V.

Table of Contents: “…Optimization of Informational Noise When Observing a Dynamic Object""; ""4.6. Example of Observing a Maneuvering Object""; ""Chapter 5. …”
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Data mining techniques : for marketing, sales, and customer relationship management by Berry, Michael J. A.

Table of Contents: “…-- The virtuous cycle of data mining -- Data mining methodology and best practices -- Data mining applications in marketing and customer relationship management -- The lure of statistics : data mining using familiar tools -- Decision trees -- Artificial neural networks -- Nearest neighbor approaches : memory-based reasoning and collaborative filtering -- Market basket analysis and association rules -- Link analysis -- Automatic cluster detection -- Knowing when to worry : hazard functions and survival analysis in marketing -- Genetic algorithms -- Data mining throughout the customer life cycle -- Data warehousing, OLAP, and data mining -- Building the data mining environment -- Preparing data for mining -- Putting data mining to work.…”
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Explorations in Computing : an Introduction to Computer Science. by Conery, John S.

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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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Space division multiple access for wireless local area networks by Vandenameele, Patrick

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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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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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Ethnic studies in academic and research libraries

Table of Contents: “…"Build it and they will come" -- Fostering transformation -- The value of integrating African American archives into undergraduate studies -- Improving representation on Wikipedia -- Returning to where it all began and connecting the dots -- Collaborative and active engagement at the Hemispheric University -- Librarians and leaders -- Crafting contemporary indigenous studies collections in the age of algorithms -- Building a Vietnamese language collection with the Vietnamese diaspora community -- Engaging with ethnic studies librarians -- For when they arrive -- BIPOC voices speak -- Supporting faculty in indigenizing curriculum and pedagogy -- Student-driven collecting efforts and initiatives -- Making spaces for ethnic studies -- Modeling Black literature -- A perspective on Asian American studies and librarianship -- Holdings in the archives are closer than they appear -- Reclaiming the Asian American history narrative -- Connecting graduate library and information studies students with ethnic studies archives.…”
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Kaplan's clinical hypertension by Kaplan, Norman M., 1931-2020, Victor, Ronald G., Flynn, Joseph T.

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Fundamentals of Parallel Computing. by Razdan, Sanjay

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Learning concurrency in Kotlin : build highly efficient and robust applications by Castiblanco Torres, Miguel Angel

Table of Contents: “…; Processes, threads, and coroutines; Processes; Threads; Coroutines; Putting things together; Introduction to concurrency; Concurrency is not parallelism; CPU-bound and I/O-bound; CPU-bound; I/O-bound; Concurrency versus parallelism in CPU-bound algorithms; Single-core execution; Parallel execution; Concurrency versus parallelism in I/O-bound algorithms; Why concurrency is often feared; Race conditions; Atomicity violation; Deadlocks; Livelocks…”
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Insect media : an archaeology of animals and technology by Parikka, Jussi, 1976-

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Design in Crisis : New Worlds, Philosophies and Practices. by Fry, Tony

Table of Contents: “…Sacrifices that do not work in a crisis -- 3. When design goes south: from decoloniality, through declassification to dessobons -- 4. …”
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Natural language processing with TensorFlow : teach language to machines using Python's deep learning library by Ganegedara, Thushan

Table of Contents: “…Implementing skip-gram with TensorFlowThe Continuous Bag-of-Words algorithm; Implementing CBOW in TensorFlow; Summary; Chapter 4: Advanced Word2vec; The original skip-gram algorithm; Implementing the original skip-gram algorithm; Comparing the original skip-gram with the improved skip-gram; Comparing skip-gram with CBOW; Performance comparison; Which is the winner, skip-gram or CBOW?…”
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Versatile Video Coding by Dominguez, Humberto Ochoa

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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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The book of Qt 4 : the art of building Qt applications by Molkentin, Daniel

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