Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships-Naacl 2016-20160422

1.Information publication:-Naacl 2016 2.What 根据小说中的人物描述,a)在每个时间段给出,人物关系的描述的概率分布,b)从时间轴上看出关系的变化轨迹,提出模型Relationship modeling network.(RMN) 3.Dataset Project Gutenberg 4.How input: 时间片段内的小说片段 output: RMN模型中的参数:关系描述的矩阵,关系描述的概率分布目标函数:小说描述的embeding 尽可能与关…

Machine Learning Algorithms Study Notes(4)—无监督学习（unsupervised learning）

1 Unsupervised Learning 1.1 k-means clustering algorithm 1.1.1 算法思想 1.1.2 k-means的不足之处 1.1.3 如何选择K值 1.1.4 Spark MLlib 实现 k-means 算法 1.2 Mixture of Gaussians and the EM algorithm 1.3 The EM Algorithm 1.4 Principal Components…

Unsupervised Learning: Use Cases

Unsupervised Learning: Use Cases Contents Visualization K-Means Clustering Transfer Learning K-Nearest Neighbors The features learned by deep neural networks can be used for the purposes of classification, clustering and regression. Neural nets are s…

Unsupervised Learning and Text Mining of Emotion Terms Using R

Unsupervised learning refers to data science approaches that involve learning without a prior knowledge about the classification of sample data. In Wikipedia, unsupervised learning has been described as "the task of inferring a function to describe h…

Supervised Learning and Unsupervised Learning

Supervised Learning In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output. Supervised learning problems are categorized…

Unsupervised learning无监督学习

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables. We can derive this structure by clustering t…

PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning --- 论文笔记

PredNet --- Deep Predictive coding networks for video prediction and unsupervised learning ICLR 2017 2017.03.12 Code and video examples can be found at: https://coxlab.github.io/prednet/ 摘要:基于监督训练的深度学习技术取得了非常大的成功,但是无监督问题仍然是一个未能解决的一大难题(从未标注的数据中学习到…

131.005 Unsupervised Learning - Cluster | 非监督学习 - 聚类

@(131 - Machine Learning | 机器学习) 零. Goal How Unsupervised Learning fills in that model gap from the original Machine Learning work flow 2.How to compare different models developed using Unsupervised Learning for their relative strengths and relative…

Unsupervised learning, attention, and other mysteries

Unsupervised learning, attention, and other mysteries Get notified when our free report “Future of Machine Intelligence: Perspectives from Leading Practitioners” is available for download. The following interview is one of many that will be included…

Coursera 机器学习第8章（上） Unsupervised Learning 学习笔记

8 Unsupervised Learning8.1 Clustering8.1.1 Unsupervised Learning: Introduction集群(聚类)的概念.什么是无监督学习:对于无标记无关联标记的数据,要求算法分析出数据的结构.什么是聚类:将未加标签的数据分成有紧密关系的子集或者簇. 做道题: ABC 8.1.2 K-Means Algorithm讨论什么是K均值以及K均值的使用.K均值算法是一种迭代的聚类方法. 用图来展示K均值更加直观:1.因为本例要将数据分为2类,所以要…

无监督学习(Unsupervised Learning)

无监督学习(Unsupervised Learning) 聚类无监督学习特点只给出了样本, 但是没有提供标签通过无监督学习算法给出的样本分成几个族(cluster), 分出来的类别不是我们自己规定的, 而是无监督学习算法自己计算出来的 K-means 聚类算法规定 $c^{(i)}$: 表示$x^{(i)}$属于哪个cluster, 如$x^{(1)}$属于$c^{(1)}$簇, 如果$c^{(1)}=1$, 则$x^{(1)}$划分在第1个类别 \(\mu_k\…

【论文阅读】Deep Clustering for Unsupervised Learning of Visual Features

文章:Deep Clustering for Unsupervised Learning of Visual Features 作者:Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze 来自于:Facebook AI Research 发表于:ECCV 2018 目录 •相关链接 •相关方法介绍 •文章出发点 •文章亮点与贡献 •方法细节 •实验结果 •分析与总结相关链接论文:https://arxiv.or…

unsupervised learning: clustering介绍

unsupervised learning 上面是监督学习与无监督学习的比较,监督学习的training set是一组带label(y)的训练集,而无监督学习不带有label(y). 上图中的监督学习求出决策线,用来区别正负样本点: clustering是unsupervised learning算法的一种,用来确定数据内部的结构. clustering算法的一些应用对客户进行分组clustering来有针对性的营销: 对社交网络(如facebook等)进行分析,找出朋友圈: 利用cluste…

Introduction - Unsupervised Learning

摘要: 本文是吴恩达 (Andrew Ng)老师<机器学习>课程,第一章<绪论:初识机器学习>中第4课时<无监督学习>的视频原文字幕.为本人在视频学习过程中逐字逐句记录下来以便日后查阅使用.现分享给大家.如有错误,欢迎大家批评指正,在此表示诚挚地感谢!同时希望对大家的学习能有所帮助. In this video (article), we'll talk about the second major type of machine learning problem, c…

supervised learning|unsupervised learning

监督学习即是supervised learning,原始数据中有每个数据有自己的数据结构同时有标签,用于classify,机器learn的是判定规则,通过已成熟的数据training model达到判断新点类型的目的. 非监督学习即是unsupervised learning,原始数据中没有附加标签,仅有数据结构,cluster的过程是机器发现相似数据结构先去找相似pattern,没有新加入的数据,仅是对原始数据的描述. …

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

目录概主要内容 Locatello F., Bauer S., Lucic M., R"{a}tsch G., Gelly S. Sch"{o}lkopf and Bachem Olivier. Challenging common assumptions in the unsupervised learning of disentangled representations. In International Conference on Machine Leaning (ICML…

论文阅读 TEMPORAL GRAPH NETWORKS FOR DEEP LEARNING ON DYNAMIC GRAPHS

14 TEMPORAL GRAPH NETWORKS FOR DEEP LEARNING ON DYNAMIC GRAPHS link:https://scholar.google.com.hk/scholar_url?url=https://arxiv.org/pdf/2006.10637.pdf%3Fref%3Dhttps://githubhelp.com&hl=zh-TW&sa=X&ei=oVakYtvtIo74yASQ1Jj4AQ&scisig=AAGBfm0bNv…

What is the difference between supervised learning and unsupervised learning?

Machine Learning is a class of algorithms which is data-driven, i.e. unlike "normal" algorithms it is the data that "tells" what the "good answer" is. Example: an hypothetical non-machine learning algorithm for face recogniti…

SfMLearner论文笔记——Unsupervised Learning of Depth and Ego-Motion from Video

1. Abstract 提出了一种无监督单目深度估计和相机运动估计的框架利用视觉合成作为监督信息,使用端到端的方式学习网络分为两部分(严格意义上是三个) 单目深度估计多视图姿态估计解释性网络(论文后面提到训练了第三个网络) 2. Introduction 计算机几何视觉难以重建真实的场景模型由于非刚性.遮挡.纹理缺失等情况的存在人类在很短的时刻可以推断自我运动以及三维场景的结构,为什么? 一个假设就是人类在移动中通过观察大量的场景,已经进化出一个对真实世界丰富的.具有结构层次的理解力…

131.008 Unsupervised Learning - Principle component Analysis |PCA | 非监督学习 - 主成分分析

@(131 - Machine Learning | 机器学习) PCA是一种特征选择方法,可将一组相关变量转变成一组基础正交变量 25 PCA的回顾和定义 Demo: when to use PCA latent features driving the patterns in the data (demo find the big shots in enron) 访问隐藏的特征 dimensionality reduction 1)visualize high dimensional dat…

131.007 Unsupervised Learning - Feature Selection | 非监督学习 - 特征选择

1 Why? Reason1 Knowledge Discovery (about human beings limitaitons) Reason2 Cause of Dimensionality (维度灾难) (about ML algorithm itself) 所需的数据量会根据你所拥有的特征数量以指数速度增长 2 NP-Hard Problem arbitrarily choose m features from n features (m≤n),don't know what m t…

131.006 Unsupervised Learning - Feature Scaling | 非监督学习 - 特征缩放

@(131 - Machine Learning | 机器学习) 1 Feature Scaling transforms features to have range [0,1] according to the formula $x' = \frac{x-x_{min}}{x_{max}-x_{min}} $ 1.1 Sklearn - MinMaxScaler from sklearn.preprocessing import MinMaxScaler import numpy weigh…

如何区分监督学习(supervised learning)和非监督学习(unsupervised learning)

监督学习:简单来说就是给定一定的训练样本(这里一定要注意,样本是既有数据,也有数据对应的结果),利用这个样本进行训练得到一个模型(可以说是一个函数),然后利用这个模型,将所有的输入映射为相应的输出,之后对输出进行简单的判断从而达到了分类(或者说回归)的问题.简单做一个区分,分类就是离散的数据,回归就是连续的数据. 非监督学习:同样,给了样本,但是这个样本是只有数据,但是没有其对应的结果,要求直接对数据进行分析建模. 比如我们去参观一个画展,我们完全对艺术一无所知,但是欣赏完多幅作品之后,我们也能…

Standford机器学习聚类算法（clustering）和非监督学习（unsupervised Learning）

聚类算法是一类非监督学习算法,在有监督学习中,学习的目标是要在两类样本中找出他们的分界,训练数据是给定标签的,要么属于正类要么属于负类.而非监督学习,它的目的是在一个没有标签的数据集中找出这个数据集的结构把它自动聚成两类或者多类. 本讲主要介绍了最常用了一种聚类算法--K-means聚类算法.如果将数据集分成两类,即k=2,K-means算法过程如下: 1.首先任意选取两个不同的样本作为两类样本的中心 2.K-means算法有两部,第一步cluster assignment step,遍历所有样…

论文笔记 Spatial contrasting for deep unsupervised learning

在我们设计无监督学习模型时,应尽量做到网络结构与有监督模型兼容有效利用有监督模型的基本模块,如dropout.relu等无监督学习的目标是为有监督模型提供初始化的参数,理想情况是"这些初始化的参数能够极大提高后续有监督模型准确率,即使有监督任务的训练样本数很少".类别理解就是,我们在Imagenet上通过有监督的方式训练得到了表达能力很强的网络,在我们迁移至新的任务时(该任务带有训练标签的样本有限),我们一般固定在Imagenet上训练好模型的前N层(N可以根据实际需要调整),然…

我的代码-unsupervised learning

# coding: utf-8 # In[1]: import pandas as pdimport numpy as npfrom sklearn import treefrom sklearn.svm import SVCfrom sklearn.grid_search import GridSearchCVfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import classificatio…