[机器学习入门] 李宏毅机器学习笔记-13 （Semi-supervised Learning ;半监督学习）

[机器学习基础] 李宏毅机器学习笔记-13 （半监督式学习方法；一种结合少量标记数据与大量未标记数据的学习技术）

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Introduction

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Why semi-supervised learning helps?

Semi-supervised Learning for Generative Model

Supervised Generative Model VS Semi-supervised Generative Model

Step

Why？

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Low-density Separation

Self-training

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Entropy-based Regularization

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Outlook: Semi-supervised SVM

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Smoothness Assumption

核心思想：近朱者赤，近墨者黑

Classify astronomy vs. travel articles

更多的数据连在一起，很难分类，那么如何做呢？

Cluster（群集 ） and then Label

这种方法 may not always make sense, especially when the category is unclear.
However, determining whether x1 and x2 are close in a high-density area requires examining if they are connected by a path of high-density regions.
Additionally, another approach involves assessing connectivity within the same density region through alternative metrics.

Graph-based Approach

Graph Construction

怎样在Graph 中定量地表示平滑度

将该式子整理一下，换个形式

如此，让smoothness 影响Loss，as a regularization term

smoothness不一定要放在output上，放到任何一层都可以。

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Better Representation

去蕪存菁，化繁為簡
Looking for Better Representation