Combining models in discrete discriminant analysis

Combining models in discrete discriminant analysis

Diverse Discrete Discriminant Analysis (DDA) models perform differently on different sample observations (Brito et al.(2006)). This fact has encouraged research in combined models for DDA. This research seems to be specially promising when the a priori classes are not well separated or when small or moderate sized samples are considered, which often occurs in practice. In this work we evaluate ...

Combining Discrete and Continuous Process Models

Two notions of process have been used in programs that reason about change* discrete models, which represent changes as instantaneous, and continuous models, which represent changes as processes that act gradually over time. We describe a technique called aggregation which unifies the two types of models and allows both to be used when performing qualitative simulation. Aggregation is a techniq...

Combining Learned Discrete and Continuous Action Models

Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, relational domains. There has also been a separate vein of work in continuous function approximation for use in optimal control in robotics. Most real world domains are grounded in continuous dynamics but also exhibit e...

Factorial Discriminant Analysis and Probabilistic Models

Face Recognition using Discrete Cosine Transform plus Linear Discriminant Analysis

recognition is a biometric identification method which among the other methods such as, finger print identification, speech recognition, signature and hand written recognition has assigned a special place to itself. In principle, the biometric identification methods include a wide range of sciences such as machine vision, image processing, pattern recognition neural networks and has various app...