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[Industry research] liuxinggaishu DL : 0: :流形学习是一种新的非监督学习方法,近年来引起越来越多机器学习和认知科学工作者的重视. 为了加深对流形学习的认识和理解,该文由流形学习的拓扑学概念入手,追溯它的发展过程. 在明确流形学习的不同表示方法后,针对几种主要的流形算法,分析它们各自的优势和不足,然后分别引用Isomap 和LL E 的应用示例. 结果表明, 流形学习较之于传统的线性降维方法,能够有效地发现非线性高维数据的本质维数,利于进行维数约简和数据分析. 最后对流形学习未来的研究方向做出展望,以期进一步拓展流形学习的应用领域.-As a new unsupervised learning met hod , manifold learning is capt uring increasing interest s of re2 searchers in the field of machine learning and cognitive sciences. To under stand manifold learning bet ter , t he topology concept of manifold learning was presented firstly , and t hen it s development history was t raced. Based on different representations of manifold , several major algorit hms were int roduced , whose advantages and defect s were pointed out respectively. Af ter that , two kinds of typical applications of Iso2 map and LL E were indicated. The result s show that compared wit h t raditional linear method , manifold learning can discover t he int rinsic dimensions of nonlinear high2dimensional data effectively , helping re2 searchers to reduce dimensionality and analyze data bet ter . Finally t he prospect of manifold learning was discussed , so as to extend t he application area of manifold learning.
Date : 2026-01-06 Size : 660kb User : 罗朝辉
[Industry research] PersonalizedRecommendationsEbusiness DL : 0: 简要介绍了电子商务推荐系统的概念、作用及组成构件,给出了推荐技术分类标准,系统综述了协同过滤推荐、基于内容推荐、基于人口统计信息推荐、基于效用推荐、基于知识推荐和基于规则推荐等6 种主要的推荐技术。对这些推荐技术的优缺点进行了比较,介绍了推荐评价技术。重点评述了电子商务个性化推荐领域中的研究热点问题,并分析了目前国内电子商务个性化推荐理论研究和应用现状,最后提出了电子商务个性化推荐领域所面临的挑战。-To make E- business system actively recommend product s to users according to their interest s , re2 search on E- business recommended systems was firstly described. Concept s , functions and constituent s of E- business recommended system were briefly int roduced. The technical recommendation standard was given. Six main recommend technologies such as collaborative filtering recommendation , recommendation based on con2 tent s , population statistics , efficiency , information and rules were mentioned. Advantages and disadvantages of these above- mentioned technical recommendations were provided. Recommendation evaluation was also int ro2 duced. Hot topics in personalized E- business recommendation research were emphasized. Then , existing prob2 lems on personalized recommendation in China were analyzed. Future research challenges facing E- business personalized recommendation were presented at last.
Date : 2026-01-06 Size : 309kb User : ming
[Industry research] basic-vision DL : 0: Color and colorspaces Numbers and Java Feature detection Globally define thresholds Self-calibrate for different lights Use the gimp/bot client on real images Getting an image performs a copy Int[] = bufferedImage.getRGB(…) Getting a pixel performs a multiplication int v = bufferedImage.RGB(x,y) offset = y*width + x Memory in rows, not columns…so go across rows and then down columns-Color and colorspaces Numbers and Java Feature detection Globally define thresholds Self-calibrate for different lights Use the gimp/bot client on real images Getting an image performs a copy Int[] = bufferedImage.getRGB(…) Getting a pixel performs a multiplication int v = bufferedImage.RGB(x,y) offset = y*width + x Memory in rows, not columns…so go across rows and then down columns
Date : 2026-01-06 Size : 399kb User : bou33aza