Dòng Nội dung
1
A gorithm to build fuzzuy decision tree for data classification problem based on fuzziness intervals matching / Le Van Tuong Lan, Nguyen Mau Han, Nguyen Cong Hao // Tạp chí Tin học và Điều khiển học . - 2016. - P. 367-380. - ISSN:


14 p.
Ký hiệu phân loại (DDC): 512
In this paper using fuzziness intervals matching with hedge algebra, the authors proposed an inductive learning method to obtain a fuzzy decision tree with high predictability
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2
A reformed K-Nearest neighbors algorithm for big data sets / Vo Ngoc Phu, Vo Thi Ngoc Tran // Journal of Computer Science. - . - Vol. 14, Issue 9, P.1213-1225. - ISSN:

New York : Science Publications, 2018
13 p.
Ký hiệu phân loại (DDC): 004
A Data Mining Has Already Had Many Algorithms Which A K-Nearest Neighbors Algorithm, K-NN, Is A Famous Algorithm For Researchers. K-NN Is Very Effective On Small Data Sets, However It Takes A Lot Of Time To Run On Big Datasets. Today, Data Sets Often Have Millions Of Data Records, Hence, It Is Difficult To Implement K-NN On Big Data. In This Research, We Propose An Improvement To K-NN To Process Big Datasets In A Shortened Execution Time. The Reformed K-Nearest Neighbors Algorithm (R-K-NN) Can Be Implemented On Large Datasets With Millions Or Even Billions Of Data Records. R-K-NN Is Tested On A Data Set With 500,000 Records. The Execution Time Of R-K-NN Is Much Shorter Than That Of K-NN. In Addition, R-K-NN Is Implemented In A Parallel Network System With Hadoop Map (M) And Hadoop Reduce (R).
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3
An introduction to data science / Jeffrey S. Saltz, Jeffrey M. Stanton
Thousand Oaks, California : SAGE Publications, Inc, 2018
168 p.
Ký hiệu phân loại (DDC): 005.74
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4
An Introduction to Machine Learning / Miroslav Kubat
Cham : Springer, 2017
348 p. ; cm.
Ký hiệu phân loại (DDC): 006.3
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of "boosting," how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.
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5
Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with apache spark / Ahmed Sherif, Amrith Ravindra
Birmingham, UK : Packt Publishing, 2018
462 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 006.31
With the help of the Apache Spark Deep Learning Cookbook, you'll work through specific recipes to generate outcomes for deep learning algorithms, without getting bogged down in theory. From setting up Apache Spark for deep learning to implementing types of neural net, this book tackles both common and not so common problems to perform deep learning on a distributed environment. In addition to this, you'll get access to deep learning code within Spark that can be reused to answer similar problems or tweaked to answer slightly different problems. You will also learn how to stream and cluster your data with Spark. Once you have got to grips with the basics, you'll explore how to implement and deploy deep learning models, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) in Spark, using popular libraries such as TensorFlow and Keras
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