Introduction to pattern recognition. Classification and
Generalization capability. Deduction and induction. Induction
principle over normed spaces. Metric selection. Non-metric spaces. Point to
point, point to cluster and cluster to cluster proximity measures. Mahalanobis
Representation and pre-processing functions. Normalization.
Missing Data. Ordinal and nominal discrete data. PCA.
Clustering algorithms: k-means and BSAS. Reinforcement
Learning BSAS. The cluster validity problem; sensitivity index; relative
validation indexes; Davies-Bouldin index, Silhouette index; clustering
algorithms based on scale parameters; stability indexes; optimized clustering
algorithms; constrained and unconstrained unsupervised modelling problems.
Hierarchical clustering. Agent based clusters mining. Consensus. Introduction to fuzzy logic. Fuzzy clustering.
Classification systems: performance and sensitivity measures.
Bayesian classifiers. Decision surfaces and discriminant functions
characterization in the Gaussian case. Maximum likelihood estimation technique.
Non-parametric estimation techniques. Classification model synthesis based on
cluster analysis. Decision trees. Robust classification: voting techniques. Ensembles
Structured data taxonomy. Dissimilarity measures on
structured data. Data fusion. Variable length domains: sequences of events,
graphs. Bellman optimality principle; edit distance. Dissimilarity measures on
labelled graph spaces (Graph Matching). Template matching techniques. Basic
algorithms for image segmentation. Region descriptors: moments and geometric
Automatic Feature selection algorithms. Introduction to
Granular Computing. Data Mining and Knowledge Discovery. Metric learning. Local metrics. Representations in
dissimilarity spaces. Symbolic histograms.
Case studies and applications: signature recognition, text
categorization and natural language processing, computer vision and image classification
systems, mechanical diagnostics systems, pattern recognition in bioinformatics,
trend prediction for financial time series.
Hardware acceleration on FPGAs and GPUs.
- Teacher: Antonello Rizzi