Examples for solving pattern classification problems in Python (IPython Notebooks)
# Sections • Statistical Pattern Recognition
• Supervised Learning
• Parametric Techniques
• Univariate Normal Density
• Multivariate Normal Density
• Non-Parametric Techniques
• Unsupervised Learning
• Techniques for Dimensionality Reduction
• Feature Selection
• Sequential Feature Selection Algorithms
• Projection
• Component Analyses
• Linear Transformation
• Principal Component Analysis (PCA)
• Multiple Discriminant Analysis (MDA)
# Statistical Pattern Recognition ## Supervised Learning ### Parametric Approach #### Univariate Normal Density
## Example 1
- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Univariate data
- 2-class problem
- equal variances
- equal priors
- Gaussian model (2 parameters)
- No Risk function
View IPython Notebook
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- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Univariate data
- 2-class problem
- different variances
- equal priors
- Gaussian model (2 parameters)
- No Risk function
View IPython Notebook
Download PDF
- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Univariate data
- 2-class problem
- equal variances
- different priors
- Gaussian model (2 parameters)
- No Risk function
View IPython Notebook
Download PDF
- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Univariate data
- 2-class problem
- different variances
- different priors
- Gaussian model (2 parameters)
- With conditional Risk (loss functions)
View IPython Notebook
Download PDF
- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Univariate data
- 2-class problem
- different variances
- equal priors
- Cauchy model (2 parameters)
- With conditional Risk (1-0 loss functions)
View IPython Notebook
Download PDF
#### Multivariate Normal Density
- Statistical Pattern Recognition
- Supervised Learning
- Parametric Learning
- Bayes Decision Theory
- Multivariate data (2-dimensional)
- 2-class problem
- different variances
- equal prior probabilities
- Gaussian model (2 parameters)
- with conditional Risk (1-0 loss functions)
View IPython Notebook
Download PDF
#Techniques for Dimensionality Reduction
View IPython Notebook
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