Examples for solving pattern classification problems in Python (IPython Notebooks)
# Sections • Statistical Pattern Recognition Examples
• 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)
• Techniques for Parameter Estimation
• Parametric Techniques
• Maximum Likelihood Estimate (MLE)
• Non-Parametric Techniques
• Parzen-window technique
# Statistical Pattern Recognition ## Supervised Learning ### Parametric Techniques #### 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
Download PDF
- 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
Download PDF
## Techniques for Parameter Estimation
### Parametric Techniques ### Maximum Likelihood Estimate (MLE)
 ### Non-Parametric Techniques
### Parzen-window technique
