Docsity
Log inSign up
Docsity
Prepare for your exams
Prepare for your exams

Study with the several resources on Docsity

Earn points to download
Earn points to download

Earn points by helping other students or get them with a premium plan

Guidelines and tips
Guidelines and tips
Sell on Docsity
Sell on Docsity
Docsity AI
Docsity AI
Log inSign up

Prepare for your exams

Study with the several resources on Docsity

Find documents

Prepare for your exams with the study notes shared by other students like you on Docsity

Search for your university

Find the specific documents for your university's exams

Docsity AINEW

Summarize your documents, ask them questions, convert them into quizzes and concept maps

Explore questions

Clear up your doubts by reading the answers to questions asked by your fellow students

Earn points to download

Earn points by helping other students or get them with a premium plan

Share documents
download point icon20 Points

For each uploaded document

Answer questions
download point icon5 Points

For each given answer (max 1 per day)

All the ways to get free points
Get points immediately

Choose a premium plan with all the points you need

Study Opportunities

Choose your next study program

Get in touch with the best universities in the world. Search through thousands of universities and official partners

Community

Ask the community

Ask the community for help and clear up your study doubts

Free resources

Our save-the-student-ebooks!

Download our free guides on studying techniques, anxiety management strategies, and thesis advice from Docsity tutors

Machine Learning Algorithms Summary, Exercises of Artificial Intelligence

Central University of Jammu and KashmirArtificial Intelligence

A concise summary of various machine learning algorithms, including naive bayes, decision trees, k-nearest neighbor, perceptron, support vector machines (svm), neural nets, regression trees, and nearest neighbor regression. It covers key aspects such as input, output, learning methods, complexity control, and notes for each algorithm.

Typology: Exercises

2011/2012

Uploaded on 07/31/2012

shaina_44kin
shaina_44kin 🇮🇳

3.9

(9)

64 documents

1 / 1

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
ML Notes
Algorithm Input Output Learns by Complexity Control Notes
Naive Bayes
boolean feature vector
discrete
estimating conditional
probabilities (counting)
Assumes independent
features
LaPlace Correction, XOR
basic Decision Tree
boolean feature vector
discrete
minimizing average
entropy at the branches
parametric: leaf size,
minimum entropy
Continuous-Valued Decision
Tree
real feature vector
discrete
minimizing average
entropy at the branches
K-Nearest Neighbor
real feature vector
discrete
memorizing all points
parametric: K
Scaling
Perceptron
real feature vector
discrete
maximizing margin
(weight space search)
limited to linear separator
guarantees separator if it
exists
SVM
real feature vector
discrete
maximizing margin
(quadratic
programming)
maximizes margin in error
function
Neural Net
real feature vector
discrete
gradient descent (weight
space search)
architecture
Architecture, Scaling
Neural Net Regression
real feature vector
real
gradient descent (weight
space search)
architecture
Architecture, Scaling
Regression Trees
real feature vector
real
minimizing variance at
the branches
Kernel functions (not SVM
kernels)
Nearest Neighbor Regression
real feature vector
real
memorizing all points
Kernel functions (not SVM
kernels)
docsity.com

Partial preview of the text

Download Machine Learning Algorithms Summary and more Exercises Artificial Intelligence in PDF only on Docsity!

ML Notes

Algorithm Input Output Learns by Complexity Control Notes

Naive Bayes boolean feature vector discrete estimating conditional probabilities (counting)

Assumes independent features

LaPlace Correction, XOR

basic Decision Tree boolean feature vector discrete minimizing average entropy at the branches

parametric: leaf size, minimum entropy Continuous-Valued Decision Tree

real feature vector discrete minimizing average entropy at the branches K-Nearest Neighbor real feature vector discrete memorizing all points parametric: K Scaling Perceptron real feature vector discrete maximizing margin (weight space search)

limited to linear separator guarantees separator if it exists SVM real feature vector discrete maximizing margin (quadratic programming)

maximizes margin in error function

Neural Net real feature vector discrete gradient descent (weight space search)

architecture Architecture, Scaling

Neural Net Regression real feature vector real gradient descent (weight space search)

architecture Architecture, Scaling

Regression Trees real feature vector real minimizing variance at the branches

Kernel functions (not SVM kernels) Nearest Neighbor Regression real feature vector real memorizing all points Kernel functions (not SVM kernels)

docsity.com