Machine Learning in Non-Stationary Environments

Machine Learning in Non-Stationary Environments pdf epub mobi txt 电子书 下载 2025

出版者:
作者:Sugiyama, Masashi; Kawanabe, Motoaki;
出品人:
页数:280
译者:
出版时间:2012-4
价格:$ 50.85
装帧:
isbn号码:9780262017091
丛书系列:
图书标签:
  • machine 
  • learning 
  • 機器學習 
  • 日本 
  • 數學 
  • 因果論 
  • 人工智能 
  • TML 
  •  
想要找书就要到 小美书屋
立刻按 ctrl+D收藏本页
你会得到大惊喜!!

As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice. Machine learning methods are usually based on the assumption that the data generation mechanism does not change over time. Yet real-world applications of machine learning, including image recognition, natural language processing, speech recognition, robot control, and bioinformatics, often violate this common assumption. Dealing with non-stationarity is one of modern machine learning's greatest challenges. This book focuses on a specific non-stationary environment known as covariate shift, in which the distributions of inputs (queries) change but the conditional distribution of outputs (answers) is unchanged, and presents machine learning theory, algorithms, and applications to overcome this variety of non-stationarity. After reviewing the state-of-the-art research in the field, the authors discuss topics that include learning under covariate shift, model selection, importance estimation, and active learning. They describe such real world applications of covariate shift adaption as brain-computer interface, speaker identification, and age prediction from facial images. With this book, they aim to encourage future research in machine learning, statistics, and engineering that strives to create truly autonomous learning machines able to learn under non-stationarity.

具体描述

读后感

评分

评分

评分

评分

评分

用户评价

评分

其实和之前的那本论文集差不多,虽然整理成章节的形式,可能还不如论文集的那本好懂

评分

其实和之前的那本论文集差不多,虽然整理成章节的形式,可能还不如论文集的那本好懂

评分

其实和之前的那本论文集差不多,虽然整理成章节的形式,可能还不如论文集的那本好懂

评分

其实和之前的那本论文集差不多,虽然整理成章节的形式,可能还不如论文集的那本好懂

评分

其实和之前的那本论文集差不多,虽然整理成章节的形式,可能还不如论文集的那本好懂

本站所有内容均为互联网搜索引擎提供的公开搜索信息,本站不存储任何数据与内容,任何内容与数据均与本站无关,如有需要请联系相关搜索引擎包括但不限于百度google,bing,sogou

© 2025 book.quotespace.org All Rights Reserved. 小美书屋 版权所有