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MLearningLecture
- 机器学习及其挑战，内容包括：机器学习及其重要性；机器学习角色的转变；五个挑战问题。研究机器学习的兄弟们要看看了。-machine learning and its challenges, including : machine learning and its importance; Machine learning the changing roles; five challenges. The study machine learning to look at the brothers ha
AdaBoost
- 这里是关于图像处理之机器学习方面的资料--AdaBoost，自适应boosting. 非常经典的资料-Here is on image processing of machine learning information- AdaBoost, adaptive boosting. Very classical information
FaceDe
- 基于支持向量聚类的多聚焦图像融合算法. 从无监督机器学习角度提出了一种基于SVC(support vector clustering)的图像融合规则,解决了基于 SVM(support vector machine)的融合规则在处理多聚焦图像融合问题时所引起的区域混叠与非平滑过渡问题,进一步提高了融合图像的质量.-Based on support vector clustering algorithm for multi-focus image fusion. Never oversig
The_Status_Quo_of_Machine_Learning_of_Artificial_I
- 机器学习是人工智能的一个子领域，是人工智能中非常活跃且范围甚广的主要核心研究领域之一，也是现代智能系统的关键环节和瓶颈。机器学习吸取了人工智能、概率统计、计算复杂性理论、控制论、信息论、哲学、生理学、神经生物学等学科的成果，主要关注于开发一些让计算机可以自动学习的技术，并通过经验提高系统自身的性能。本文介绍了机器学习的概念、基本结构和发展，以及各种机器学习方法，包括机械学习、归纳学习、类比学习、解释学习、基于神经网络的学习以及知识发现等，并简单叙述了机器学习的相关算法，包括决策树算法、随机森林算
machinelearning
- 机器学习和数据挖掘有密切的联系，本文放在一起做一个粗浅的介绍-Machine learning and data mining are closely linked, the paper together to make a crude descr iption
belkniyogi
- Belkin and Niyogi s Manifold Regularization paper (field is machine learning / manifold learning).
mlss_slides
- Slides Machine learning Sumerschool 2009
Statistical-Learning-Theory
- 此文章是介绍支持向量机的经典书籍，对使用支持向量机的同行们很有帮助-This article is to introduce the support vector machine classic books on the use of support vector machines helpful colleagues
liuxinggaishu
- :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
seminarpapers
- semiars The World Wide Web, which has started as a document repository, is rapidly transforming to a full fledged virtual environment that facilitates services, interaction, and com- munication. Under this light, the Semantic Web and Web 2.
semi-supervized-learning
- A PhD thesis on Semi-supervised learning with Graphs by Xiaojin Zhu. Focuses on creating graphs, based on a mixture of labeled and unlabeled data (as per the semi-supervised learning paradigm) and using processes on these graphs to propagate in rigo
Bayesian-Reasoning-and-Machine-Learning.pdf
- Bayesian Reasoning and Machine Learning
Credit-Scoring-Using-Machine-Learning
- Credit Scoring Using Machine Learning
[Hans_Georg_Schaathun(auth.)]_Machine-learning-in
- Machine Learning Book for Beginner
Pattern-Recognition-and-Machine-Learning
- Pattern Recognition and Machine Learning
machine-learning-of--stanford
- 斯坦福大学的机器学习课件，全英文的。学习用-PowerPoint of machine learning lessons stanford
machine-learning-of-xibeigongye
- 机器学习课件，西北工业大学的，全英文ppt，没有密码-machine learning lessons of xibei gongye daxue
Machine-Learning-
- Efficient Learning of Image Super-resolution and Compression Artifact Removal with Semi-Local Gaussian Proce-Efficient Learning of Image Super-resolution and Compression Artifact Removal with Semi-Local Gaussian Process
2-learning
- Reading text photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been pro
challenge-in-machine-learning
- 本文主要介绍的是机器学习的挑战，并介绍了微软的Azure（机器学习云服务）。-This paper describes the challenges of machine learning, and describes the Microsoft azure (machine learning cloud services)