找回密碼
 To register

QQ登錄

只需一步,快速開始

掃一掃,訪問微社區(qū)

打印 上一主題 下一主題

Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical

[復制鏈接]
查看: 47580|回復: 62
樓主
發(fā)表于 2025-3-21 19:26:10 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Data Science and Predictive Analytics
副標題Biomedical and Healt
編輯Ivo D. Dinov
視頻videohttp://file.papertrans.cn/264/263104/263104.mp4
概述A novel transdisciplinary treatise of predictive health analytics.Complete and self-contained treatment of the theory, experimental modeling, system development, and validation of predictive health an
圖書封面Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 20181st edition Ivo D. Dinov 2018 big data.R.statistical
描述Over the past decade, Big Data have become ubiquitous in all economic sectors, scientific disciplines, and human activities. They have led to striking technological advances, affecting all human experiences. Our ability to manage, understand, interrogate, and interpret such extremely large, multisource, heterogeneous, incomplete, multiscale, and incongruent data has not kept pace with the rapid increase of the volume, complexity and proliferation of the deluge of digital information. There are three reasons for this shortfall. First, the volume of data is increasing much faster than the corresponding rise of our computational processing power (Kryder’s law > Moore’s law). Second, traditional discipline-bounds inhibit expeditious progress. Third, our education and training activities have fallen behind the accelerated trend of scientific, information, and communication advances. There are very few rigorous instructional resources, interactive learning materials, and dynamic trainingenvironments that support active data science learning. The textbook balances the mathematical foundations with dexterous demonstrations and examples of data, tools, modules and workflows that serve as pi
出版日期Textbook 20181st edition
關鍵詞big data; R; statistical computing; predictive analytics; data science; health analytics; machine learning
版次1
doihttps://doi.org/10.1007/978-3-319-72347-1
isbn_softcover978-3-030-10187-9
isbn_ebook978-3-319-72347-1
copyrightIvo D. Dinov 2018
The information of publication is updating

書目名稱Data Science and Predictive Analytics影響因子(影響力)




書目名稱Data Science and Predictive Analytics影響因子(影響力)學科排名




書目名稱Data Science and Predictive Analytics網絡公開度




書目名稱Data Science and Predictive Analytics網絡公開度學科排名




書目名稱Data Science and Predictive Analytics被引頻次




書目名稱Data Science and Predictive Analytics被引頻次學科排名




書目名稱Data Science and Predictive Analytics年度引用




書目名稱Data Science and Predictive Analytics年度引用學科排名




書目名稱Data Science and Predictive Analytics讀者反饋




書目名稱Data Science and Predictive Analytics讀者反饋學科排名




單選投票, 共有 0 人參與投票
 

0票 0%

Perfect with Aesthetics

 

0票 0%

Better Implies Difficulty

 

0票 0%

Good and Satisfactory

 

0票 0%

Adverse Performance

 

0票 0%

Disdainful Garbage

您所在的用戶組沒有投票權限
沙發(fā)
發(fā)表于 2025-3-22 00:03:03 | 只看該作者
板凳
發(fā)表于 2025-3-22 02:37:39 | 只看該作者
地板
發(fā)表于 2025-3-22 05:15:25 | 只看該作者
5#
發(fā)表于 2025-3-22 12:42:14 | 只看該作者
Linear Algebra & Matrix Computing,is generally challenging to visualize complex data, e.g., large vectors, tensors, and tables in n-dimensional Euclidian spaces (.?≥?3). Linear algebra allows us to mathematically represent, computationally model, statistically analyze, synthetically simulate, and visually summarize such complex data
6#
發(fā)表于 2025-3-22 13:11:01 | 只看該作者
Dimensionality Reduction,ber of features when modeling a very large number of variables. Dimension reduction can help us extract a set of “uncorrelated” principal variables and reduce the complexity of the data. We are not simply picking some of the original variables. Rather, we are constructing new “uncorrelated” variable
7#
發(fā)表于 2025-3-22 17:55:42 | 只看該作者
8#
發(fā)表于 2025-3-23 00:39:17 | 只看該作者
9#
發(fā)表于 2025-3-23 04:40:02 | 只看該作者
Decision Tree Divide and Conquer Classification,les. In some cases, we need to specify well stated rules for our decisions, just like a scoring criterion for driving ability or credit scoring for loan underwriting. The decisions in many situations actually require having a clear and easily understandable decision tree to follow the classification
10#
發(fā)表于 2025-3-23 08:58:00 | 只看該作者
Forecasting Numeric Data Using Regression Models, this Chapter, we will focus on specific model-based statistical methods providing forecasting and classification functionality. Specifically, we will (1) demonstrate the predictive power of multiple linear regression; (2) show the foundation of regression trees and model trees; and (3) examine two
 關于派博傳思  派博傳思旗下網站  友情鏈接
派博傳思介紹 公司地理位置 論文服務流程 影響因子官網 吾愛論文網 大講堂 北京大學 Oxford Uni. Harvard Uni.
發(fā)展歷史沿革 期刊點評 投稿經驗總結 SCIENCEGARD IMPACTFACTOR 派博系數 清華大學 Yale Uni. Stanford Uni.
QQ|Archiver|手機版|小黑屋| 派博傳思國際 ( 京公網安備110108008328) GMT+8, 2025-10-7 16:04
Copyright © 2001-2015 派博傳思   京公網安備110108008328 版權所有 All rights reserved
快速回復 返回頂部 返回列表
团风县| 锦屏县| 揭阳市| 固原市| 洛宁县| 南乐县| 宝丰县| 鹤岗市| 永嘉县| 西乌| 宁远县| 萍乡市| 扎兰屯市| 修文县| 太康县| 黄骅市| 平湖市| 德保县| 浑源县| 洪江市| 江达县| 灌阳县| 包头市| 新田县| 区。| 太原市| 禹州市| 高州市| 清徐县| 大石桥市| 河源市| 会宁县| 潜江市| 思南县| 崇文区| 鄂尔多斯市| 连城县| 周宁县| 福建省| 淮阳县| 万年县|