Introduction to Bayesian Decision Theory and Statistical Machine Learning Techniques

📚 In pattern classification, the first step is feature extraction, followed by feature selection to identify the most discriminative features.

💡 Based on the selected features, a feature vector is created and used for classification using machine learning techniques.

🔁 System evaluation provides feedback to improve the performance of the classification system.

Hard decision and soft decision in classification

Difference between rigid and flexible decision boundaries

Importance of discriminative features and feature selection for pattern classification

📚 Pattern classification involves determining the corresponding class from a given measurement, which is not a one-to-one mapping.

🔎 Overlapping occurs in pattern classification when patterns from different classes share common attributes.

⚖️ Statistical pattern classification involves determining the probability of obtaining a class given a feature vector.

🔢 The discriminate function is used to partition the D-dimensional space for classifying feature vectors.

🔀 The decision rule assigns a feature vector to a class based on the discriminate function.

⚖️ The decision boundary is defined by the equation of the discriminate function.

🧮 Linear discriminate function is represented by an equation involving a weight vector and bias.

🔬 Statistical pattern classification is derived from the base law in probability theory.

⭐ Classification is different from regression, as it deals with discrete levels denoting classes.

⚖️ Bayesian Decision Theory is a powerful tool for decision making in pattern classification.

📚 To improve classification accuracy, additional information such as features and class conditional density can be considered.

📚 Bayesian Decision Theory involves using Bayes' theorem to calculate posterior probabilities.

🔢 The classification decision is based on comparing the likelihood and the prior probabilities.

❓ The probability of error is used to determine the optimal classification decision.

📚 The concept of Bayesian Decision Theory involves selecting a class based on the probability of error.

🔢 The average probability of error is determined by integrating the probability of error given X over all possible values of X.

⚖️ The objective of the base classifier is to minimize the probability of error for every value of X.

Bayesian decision theory is used for classification based on posterior probabilities.

The decision is made by selecting the class with the highest posterior probability.

Loss and risk are defined to evaluate the decision-making process.