Foundations of Machine Learning: Rademacher complexity and VC-Dimension(1) 前面两篇文章中,我们在给出PAC-learnable定理时,都有一个前提假设,那就是 Hypothesis set 是有限的.但很明显,在实际中的假设集大都是无限的,比如上一篇文章中介绍的与坐标轴对齐的矩阵的例子,其 Hypothesis set 就是无限的. 假设我们也用上一章的方法来分析,最后得到的上界中含有无穷大的项$log|H|$, 显然这…
对于一个concept class C,如果存在一个算法A和一个多项式poly(.,.,.,.),有对于任意的ε>0.δ>0以及X的任意分布D和任何target concept C,当sample size m>=poly(1/ε,1/δ,n,size(c))时,不等式: 都成立,那么就说这个concept class C是PAC-learnable的. (1).n:x的维度. (2).size(c): O(n):an upper bound on the cost of the com…
读论文 Neural Machine Translation by Jointly Learning to Align and Translate 这个论文是在NLP中第一个使用attention机制的论文.他们把attention机制用到了神经网络机器翻译(NMT)上.NMT其实就是一个典型的sequence to sequence模型,也就是一个encoder to decoder模型,传统的NMT使用两个RNN,一个RNN对源语言进行编码,将源语言编码到一个固定维度的中间向量,然后在使用一…
Deep Learning and Shallow Learning 由于 Deep Learning 现在如火如荼的势头,在各种领域逐渐占据 state-of-the-art 的地位,上个学期在一门课的 project 中见识过了 deep learning 的效果,最近在做一个东西的时候模型上遇到一点瓶颈于是终于决定也来了解一下这个魔幻的领域. 据说 Deep Learning 的 break through 大概可以从 Hinton 在 2006 年提出的用于训练 Deep Belief…
Predictive learning vs. representation learning 预测学习 与 表示学习 When you take a machine learning class, there's a good chance it's divided into a unit on supervised learning and a unit on unsupervised learning. We certainly care about this distinction f…
by Jason Brownlee on December 20, 2017 in Better Deep Learning Transfer learning is a machine learning method where a model developed for a task is reused as the starting point for a model on a second task. It is a popular approach in deep learning w…
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…
1. active learning Active learning 是一种特殊形式的半监督机器学习方法,该方法允许交互式地询问用户(或者其他形式的信息源 information source)以获取对新的数据样本的理想输出. Active learning 提供的这种交互机制尤其适用于 unlabeled data 有很多,且手工标注的代价十分高昂的场合.显然这种交互式地向用户询问以获取label,使得原始非监督问题变成了一种迭代式的监督学习(iterative supervised lear…
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…
参考文献: 摘于上文献: The more general and powerful setting is the self-taught learning setting, which does not assume that your unlabeled data xu has to be drawn from the same distribution as your labeled data xl. The more restrictive setting where the unlab…
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node26.html [平均-打折奖励] Schwartz [106] examined the problem of adapting Q-learning to an average-reward framework. Although his R-learning algorithm seems to exhibit convergence p…
https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…
