Ensemble Machine Learning Advances in Research and Applications
Category: eBooks
- views: 99
- date: 7 January 2026
- posted by: BeMyLove
Ensemble Machine Learning Advances in Research and Applications | 10.65 MB
Title: someTitle
Author: someAuthor
Category: Nonfiction, Computers, Database Management, Science & Nature, Technology, Engineering, General Computing
Language: English | 287 Pages | ISBN: 9811689326
Description:
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed "ensemble learning" by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as "boosting" and "random forest" facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics.
Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
DOWNLOAD:
https://rapidgator.net/file/6423171f97cef496457789a93cae8e36/Ensemble_Machine_Learning_Advances_in_Research_and_Applications.pdf
https://nitroflare.com/view/D1714334BB46368/Ensemble_Machine_Learning_Advances_in_Research_and_Applications.pdf
It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed "ensemble learning" by researchers in computational intelligence and machine learning, it is known to improve a decision system's robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as "boosting" and "random forest" facilitate solutions to key computational issues such as face recognition and are now being applied in areas as diverse as object tracking and bioinformatics.
Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike.
DOWNLOAD:
https://rapidgator.net/file/6423171f97cef496457789a93cae8e36/Ensemble_Machine_Learning_Advances_in_Research_and_Applications.pdf
https://nitroflare.com/view/D1714334BB46368/Ensemble_Machine_Learning_Advances_in_Research_and_Applications.pdf
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||