http://www.cs.princeton.edu/~blei/topicmodeling.html Topic models are a suite of algorithms that uncover the hidden thematic structure in document collections. These algorithms help us develop new ways to search, browse and summarize large archives o…
Entity Linking with Effective Acronym Expansion, Instance Selection and Topic Modeling 一.主要贡献 1. propose a supervised learning algorithm to expand more complicated acronyms 2. propose an instance selection strategy to effectively utilize the automati…
此文为David M. Blei所写的<Introduction to Probabilistic Topic Models>的译文,供大家参考. 摘要:概率主题模型是一系列旨在发现隐藏在大规模文档中的主题结构的算法.本文首先回顾了这一领域的主要思想,接着调研了当前的研究水平,最后展望某些有所希望的方向.从最简单的主题模型——潜在狄立克雷分配(Latent Dirichlet Allocation,LDA)出发,讨论了其与概率建模的联系,描述了用于主题发现的两种算法.主题模型日新月异,被扩展和…
1.Tang, Jian, et al. "Understanding the Limiting Factors of Topic Modeling via Posterior Contraction Analysis." ICML. 2014. This is the best paper of ICML 2014. The first author is Jian Tang(his weibo:http://weibo.com/1741301241, Phd of Peking…
概率主题模型简介 Introduction to Probabilistic Topic Models 转:http://www.cnblogs.com/siegfang/archive/2013/01/30/2882391.html 此文为David M. Blei所写的<Introduction to Probabilistic Topic Models>的译文,供大家参考. 摘要:概率主题模型是一系列旨在发现隐藏在大规模文档中的主题结构的算法.本文首先回顾了这一领域的主要思…
最近在学深度学习相关的东西,在网上搜集到了一些不错的资料,现在汇总一下: Free Online Books by Yoshua Bengio, Ian Goodfellow and Aaron Courville Neural Networks and Deep Learning42 by Michael Nielsen Deep Learning27 by Microsoft Research Deep Learning Tutorial23 by LISA lab, University…
转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…
本文转载自:https://www.cnblogs.com/colipso/p/4284510.html 好文 mark http://www.52nlp.cn/python-%E7%BD%91%E9%A1%B5%E7%88%AC%E8%99%AB-%E6%96%87%E6%9C%AC%E5%A4%84%E7%90%86-%E7%A7%91%E5%AD%A6%E8%AE%A1%E7%AE%97-%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0-%E6%95%B0%E6%8…
