SVM

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Naive Bayesian Classifiers - Multinomial, Bernoulli and Gaussian with Solved Examples and Laplace Smoothing

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 Learn about the Naive Bayes Classifier in the following notes. Numeric Example with Dataset (Transactional Data) Sum1 Sum2 Consider the following dataset. Apply the Naïve Bayes classifier to the following frequency table and predict the type of fruit given it is {Yellow, Sweet, Long}. Solution can be viewed in the following pdf. Numeric Example with Text Data Multinomial Naive Bayes vs Bernoulli Naive Bayes Multinomial Naive Bayes and Bernoulli Naive Bayes are both variations of the Naive Bayes algorithm, and they are used for different types of data distributions: 1. Multinomial Naive Bayes:    - The Multinomial Naive Bayes classifier is used for data that is multinomially distributed, which typically means it is used for discrete data.    - It is particularly suitable for text classification problems where features (or words) can occur multiple times. For example, it can be used for document classification where the features are the frequencies...
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Lesson4.1 : Designing a Learning System for Checkers Problem - The Training Experience

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Goal : Design program to play checkers and compete in world checkers tournament. There are various steps to design a system that learns to play checkers. This blog focuses on the first step. Step 1 : Choosing the Training Experience The effectiveness of a learner's training hinges greatly on the type of training experience it receives. Different training experiences can lead to drastically different outcomes, with some facilitating success and others paving the way for failure.   Type of training data used: The challenge of designing a learning system for playing checkers can be well-understood through the lens of the type of training data used: direct training examples versus indirect information. Both methods have their unique challenges and advantages, and they contribute differently to the system's learning process. Direct Training Examples In the context of checkers, direct training examples would consist of specific board states paired with the best possible move for each...
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Lesson5.9. Candidate Elimination Algorithm

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  Consider the following dataset: Training Dataset A person named Aldo Enjoys sport under the given conditions. Concept The concept to learn is when does a person Aldo enjoy sports. The answer is Boolean : Yes – enjoys sport or No – doesn’t enjoy sport. Hypothesis Space The actual space in which we search is huge. So let us restrict to a hypothesis representation of the search space with only the attributes in the training dataset. The easiest way to represent is by taking the conjunction of all the attributes. That is, <sunny, warm, normal, strong, warm, same, yes> is a hypothesis represented by <x,c(x)>. Here c(x) is ‘yes’. We say that if it is, Sunny and warm and normal and strong and warm and same, then Aldo enjoys sports. Notations We assume the following notations as before: 0 – most specific and implies that the attribute should have no value. Attribute name – the given attributes in the table. Eg. sunny ? – the most generic where ...
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