# occam's razor

Published articles for occam's razor.

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## The Boosting Margin, or Why Boosting Doesn't Overfit

DevFeed: [The Boosting Margin, or Why Boosting Doesn't Overfit](<https://devfeed.tech/articles/the-boosting-margin-or-why-boosting-doesn-t-overfit-40388.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2015/09/21/the-boosting-margin-or-why-boosting-doesnt-overfit/>)

Published: 2015-09-21T11:33:00Z

Content type: article

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [machine learning overfitting](<https://devfeed.tech/topics/machine-learning-overfitting.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [generalization in machine learning](<https://devfeed.tech/topics/generalization-in-machine-learning.md>), [Occam's razor machine learning](<https://devfeed.tech/topics/occam-s-razor-machine-learning.md>), [Algorithm](<https://devfeed.tech/topics/algorithm.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [boosting](<https://devfeed.tech/tags/boosting.md>), [chernoff-bound](<https://devfeed.tech/tags/chernoff-bound.md>), [classficiation](<https://devfeed.tech/tags/classficiation.md>), [complexity](<https://devfeed.tech/tags/complexity.md>), [conditional-probability](<https://devfeed.tech/tags/conditional-probability.md>), [error](<https://devfeed.tech/tags/error.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [margins](<https://devfeed.tech/tags/margins.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [model](<https://devfeed.tech/tags/model.md>), [occam-s-razor](<https://devfeed.tech/tags/occam-s-razor.md>), [overfitting](<https://devfeed.tech/tags/overfitting.md>), [training-data](<https://devfeed.tech/tags/training-data.md>), [vc-dimension](<https://devfeed.tech/tags/vc-dimension.md>)

### AI overview

This article explains why boosting can continue improving generalization after reaching zero training error. It introduces the margin-based theoretical explanation for this behavior and defines the confidence and margin of AdaBoost classifiers.

### Source excerpt

There's a well-understood phenomenon in machine learning called overfitting. The idea is best shown by a graph: overfitting Let me explain. The vertical axis represents the error of a hypothesis. The horizontal axis represents the complexity of the hypothesis. The blue curve represents the error of a machine learning algorithm's output on its training data, and the red curve represents the generalization of that hypothesis to the real world. The overfitting phenomenon is marker in the middle of the graph, before which the training error and generalization error both go down, but after which the training error continues to fall while the generalization error rises.

## Occam's Razor and PAC-learning

DevFeed: [Occam's Razor and PAC-learning](<https://devfeed.tech/articles/occam-s-razor-and-pac-learning-40366.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2014/09/19/occams-razor-and-pac-learning/>)

Published: 2014-09-19T10:00:47Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Occam's razor machine learning](<https://devfeed.tech/topics/occam-s-razor-machine-learning.md>), [Learning](<https://devfeed.tech/topics/learning.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [chernoff](<https://devfeed.tech/tags/chernoff.md>), [chernoff-bound](<https://devfeed.tech/tags/chernoff-bound.md>), [computational-learning-theory](<https://devfeed.tech/tags/computational-learning-theory.md>), [learning-theory](<https://devfeed.tech/tags/learning-theory.md>), [occam-s-razor](<https://devfeed.tech/tags/occam-s-razor.md>), [pac-learning](<https://devfeed.tech/tags/pac-learning.md>), [vc-dimension](<https://devfeed.tech/tags/vc-dimension.md>)

### AI overview

This tutorial develops a simple PAC-learning theorem for a finite hypothesis class. It explains that if a hypothesis consistent with observed data can always be found, the class can be learned efficiently, and it uses the Chernoff-Hoeffding bound to quantify the sample requirement for achieving low error.

### Source excerpt

So far our discussion of learning theory has been seeing the definition of PAC-learning, tinkering with it, and seeing simple examples of learnable concept classes. We've said that our real interest is in proving big theorems about what big classes of problems can and can't be learned. One major tool for doing this with PAC is the concept of VC-dimension, but to set the stage we're going to prove a simpler theorem that gives a nice picture of PAC-learning when your hypothesis class is small.

## Probably Approximately Correct -- a Formal Theory of Learning

DevFeed: [Probably Approximately Correct -- a Formal Theory of Learning](<https://devfeed.tech/articles/probably-approximately-correct-a-formal-theory-of-learning-40337.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2014/01/02/probably-approximately-correct-a-formal-theory-of-learning/>)

Published: 2014-01-02T18:45:51Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [Learning](<https://devfeed.tech/topics/learning.md>), [Machine Learning & Artificial Intelligence](<https://devfeed.tech/topics/machine-learning-artificial-intelligence.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>)

Tags: [learning](<https://devfeed.tech/tags/learning.md>), [learning-theory](<https://devfeed.tech/tags/learning-theory.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [occam-s-razor](<https://devfeed.tech/tags/occam-s-razor.md>), [pac-learning](<https://devfeed.tech/tags/pac-learning.md>), [theory](<https://devfeed.tech/tags/theory.md>)

### AI overview

A mathematical introduction to PAC learning, a foundational framework in computational learning theory. The article develops basic definitions, explains PAC-learnability through interval examples, and places the theory in its historical context.

### Source excerpt

In tackling machine learning (and computer science in general) we face some deep philosophical questions. Questions like, "What does it mean to learn?" and, "Can a computer learn?" and, "How do you define simplicity?" and, "Why does Occam's Razor work? (Why do simple hypotheses do well at modelling reality?)" In a very deep sense, learning theorists take these philosophical questions -- or at least aspects of them -- give them fleshy mathematical bodies, and then answer them with theorems and proofs.