Skip to content
All tags

#perceptron

3 posts
aiguideReading NTU Hsuan-Tien Lin Machine Learning Foundations & Techniques

Perceptrons and PLA: How a Line Corrects Itself from Mistakes | NTU Hsuan-Tien Lin Machine Learning Foundations Lecture 2

This lecture puts the first concrete content into the flowchart from Lecture 1: the hypothesis set H is all lines (perceptrons), and the algorithm A is PLA, which adds yₙxₙ to w whenever it sees a misclassified point. When the data is linearly separable, PLA started from w₀ = 0 halts after at most R²/ρ² corrections; when the data cannot be separated, switch to pocket, which keeps the line with the fewest mistakes seen so far in its pocket.

#ntu-htlin-ml#ntu#ai-course#machine-learning#perceptron#classification
aiguideReading NTU Hsuan-Tien Lin Machine Learning Foundations & Techniques

The VC Dimension: Measuring Model Complexity | NTU Hsuan-Tien Lin Machine Learning Foundations Lecture 7

This lecture names the largest non-break point the VC dimension d_VC, proves in two parts (an invertible matrix, then linear dependence) that d-dimensional perceptrons have d_VC = d + 1, and rewrites the VC bound as E_out ≤ E_in + Ω: the larger d_VC is, the easier it is to push E_in down, but the larger the complexity penalty Ω becomes. By the bound, ε = δ = 0.1 and d_VC = 3 need 29,300 examples, roughly 10,000 times d_VC; in practice 10 times is often enough.

#ntu-htlin-ml#ntu#ai-course#machine-learning#learning-theory#perceptron
learningguideReading CMU 10-301 Introduction to Machine Learning

Compare K-NN, Perceptron, and Linear Regression | CMU 10-301 Assignment 3

HW3 is written work: a decision-tree review followed by K-NN, Perceptron, and Linear Regression through inductive bias, errors, and model selection.

#cmu#machine-learning#knn#perceptron#linear-regression