找回密码
 To register

QQ登录

只需一步,快速开始

扫一扫,访问微社区

Titlebook: Efficient Learning Machines; Theories, Concepts, Mariette Awad,Rahul Khanna Book‘‘‘‘‘‘‘‘ 2015 The Editor(s) (if applicable) and The Author

[复制链接]
查看: 49240|回复: 45
发表于 2025-3-21 18:49:28 | 显示全部楼层 |阅读模式
书目名称Efficient Learning Machines
副标题Theories, Concepts,
编辑Mariette Awad,Rahul Khanna
视频video
概述Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspi
图书封面Titlebook: Efficient Learning Machines; Theories, Concepts,  Mariette Awad,Rahul Khanna Book‘‘‘‘‘‘‘‘ 2015 The Editor(s) (if applicable) and The Author
描述.Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. .Efficient Learning Machines. explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. .Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of .Efficient Learning Machines. will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions..Advances in computing performance, storage, memory, unstructu
出版日期Book‘‘‘‘‘‘‘‘ 2015
版次1
doihttps://doi.org/10.1007/978-1-4302-5990-9
isbn_softcover978-1-4302-5989-3
isbn_ebook978-1-4302-5990-9
copyrightThe Editor(s) (if applicable) and The Author(s) 2015
The information of publication is updating

书目名称Efficient Learning Machines影响因子(影响力)




书目名称Efficient Learning Machines影响因子(影响力)学科排名




书目名称Efficient Learning Machines网络公开度




书目名称Efficient Learning Machines网络公开度学科排名




书目名称Efficient Learning Machines被引频次




书目名称Efficient Learning Machines被引频次学科排名




书目名称Efficient Learning Machines年度引用




书目名称Efficient Learning Machines年度引用学科排名




书目名称Efficient Learning Machines读者反馈




书目名称Efficient Learning Machines读者反馈学科排名




单选投票, 共有 1 人参与投票
 

0票 0.00%

Perfect with Aesthetics

 

0票 0.00%

Better Implies Difficulty

 

0票 0.00%

Good and Satisfactory

 

1票 100.00%

Adverse Performance

 

0票 0.00%

Disdainful Garbage

您所在的用户组没有投票权限
发表于 2025-3-21 22:57:05 | 显示全部楼层
Machine Learning and Knowledge Discovery, in diverse fields related to engineering, biological science, social media, medicine, and business intelligence. The primary objective for most of the applications is to characterize patterns in a complex stream of data. These patterns are then coupled with knowledge discovery and decision making.
发表于 2025-3-22 03:29:20 | 显示全部楼层
Support Vector Machines for Classification, learning model. SVM offers a principled approach to problems because of its mathematical foundation in statistical learning theory. SVM constructs its solution in terms of a subset of the training input. SVM has been extensively used for classification, regression, novelty detection tasks, and feat
发表于 2025-3-22 07:24:16 | 显示全部楼层
Support Vector Regression, presented in . can be generalized to become applicable to regression problems. As in classification, . (SVR) is characterized by the use of kernels, sparse solution, and VC control of the margin and the number of .. Although less popular than SVM, SVR has been proven to be an effective tool in real
发表于 2025-3-22 10:18:22 | 显示全部楼层
发表于 2025-3-22 13:33:51 | 显示全部楼层
发表于 2025-3-22 17:57:08 | 显示全部楼层
Deep Neural Networks,ong the many evolutions of ANN, . (DNNs) (Hinton, Osindero, and Teh 2006) stand out as a promising extension of the shallow ANN structure. The best demonstration thus far of hierarchical learning based on DNN, along with other Bayesian inference and deduction reasoning techniques, has been the perfo
发表于 2025-3-23 00:05:53 | 显示全部楼层
发表于 2025-3-23 05:26:21 | 显示全部楼层
发表于 2025-3-23 05:32:10 | 显示全部楼层
 关于派博传思  派博传思旗下网站  友情链接
派博传思介绍 公司地理位置 论文服务流程 影响因子官网 SITEMAP 大讲堂 北京大学 Oxford Uni. Harvard Uni.
发展历史沿革 期刊点评 投稿经验总结 SCIENCEGARD IMPACTFACTOR 派博系数 清华大学 Yale Uni. Stanford Uni.
|Archiver|手机版|小黑屋| 派博传思国际 ( 京公网安备110108008328) GMT+8, 2025-5-26 02:56
Copyright © 2001-2015 派博传思   京公网安备110108008328 版权所有 All rights reserved
快速回复 返回顶部 返回列表