Machine Learning 1: UNIT 3 (A) PPTs: Support Vector Machines PPTs

-

                                                                          Unit III - A

Support Vector Machines

1.     Linear SVM Classification

2.     Soft Margin Classification

3.     Nonlinear SVM Classification

4.     Polynomial Kernel

5.     Adding Similarity Features

6.     Gaussian RBF Kernel

7.     Computational Complexity

Comments

Post a Comment

Popular posts from this blog

III-I CSE(AIML) ESSENTIALS OF MACHINE LEARNING

-
  ESSENTIALS OF MACHINE LEARNING 👉 Unit – I:  Introduction to Machine Learning What is machine learning, types of machine learning, applications of machine learning, preparing to model, modeling, and evaluation        👉 UNIT-1 (A) Introduction to Machine Learning Notes 👉 UNIT-1 (A) Introduction to Machine Learning PPTs 👉 UNIT-1 (B) Introduction to Machine Learning Notes 👉 UNIT-1 (B) Introduction to Machine Learning PPTs 👉 UNIT-1 Important Long Answer Question s 👉 UNIT 1 Important Long Answer Question YouTube Link 👉 Unit – II:  Supervised Learning: Regression Introduction to Regression, Example of Regression, Simple Linear Regression, Multiple Linear Regression, Assumptions in Regression Analysis, Improving the accuracy of the Linear Regression Model, Ridge Regression, and Lasso Regression.         👉 UNIT-II Supervised Learning: Regression Notes  👉 UNIT-II Supervised Learning: Regression PPTs 👉 UNIT-2 Import...
Machine Learning

IT IV-I FUNDAMENTALS OF MACHINE LEARNING

-
  FUNDAMENTALS OF MACHINE LEARNING Unit – I Introduction andConcept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning.  Concept learning task , Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias 👉 UNIT-1 (A) Introduction Notes   👉 UNIT-1(B) Concept Learning PPTs 👉 UNIT-1 Introduction and Concept Learning Notes 👉 UNIT-1 Introduction and Concept Learning Long Answer Question 👉 UNIT-1 Introduction & Concept Learning Important Questions YouTube Link 👉 Fundamentals of Machine Learning YouTube Videos Links Unit - II Decision Treelearning and Neural Network learning : Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor,...
Machine Learning

VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM

-
Image
  VERSION SPACES AND THE CANDIDATE-ELIMINATION ALGORITHM   •         The key idea in the CANDIDATE-ELIMINATION algorithm is to output •         a description of the set of all hypotheses consistent with the training examples Note difference between definitions of consistent and satisfies ·        An example x is said to satisfy hypothesis h when h(x) = 1, regardless of whether x is a positive or negative example of the target concept . ·        An example x is said to consistent with hypothesis h iff h(x) = c(x) The LIST-THEN-ELIMINATION algorithm The LIST-THEN-ELIMINATE algorithm first initializes the version space to contain all hypotheses in H and then eliminates any hypothesis found inconsistent with any training example . ___________________________________________________________________________ 1. VersionSpace c...
Machine Learning