Abstract: Pattern recognition is about assigning objects (also called observations, instances or examples) to classes. The objects are described by features and represented as points in the feature space. A classifier is an algorithm that assigns a class label to any given point in the feature space. Pattern recognition comprises supervised learning (predefined class labels) and unsupervised le...

There have been considerable attempts to incorporate semantic knowledge into coreference resolution systems: different knowledge sources such as WordNet and Wikipedia have been used to boost the performance. In this paper, we propose new ways to extract WordNet feature. This feature, along with other features such as named entity feature, can be used to build an accurate semantic class (SC) cla...

The concept of a negative class does not apply to many problems for which classification is increasingly utilized. In this study we investigate the reliability of evaluation metrics when the negative class contains an unknown proportion of mislabeled positive class instances. We examine how evaluation metrics can inform us about potential systematic biases in the data. We provide a motivating c...

The design of a minimum risk classifier based on data usually stems from the stationarity assumption that the conditions during training and test are the same: the misclassification costs assumed during training must be in agreement with real costs, and the same statistical process must have generated both training and test data. Unfortunately, in real world applications, these assumptions may ...

In the problem of one-class classification one of the classes, called the target class, has to be distinguished from all other possible objects. These are considered as non-targets. The need for solving such a task arises in many practical applications, e.g. in machine fault detection, face recognition, authorship verification, fraud recognition or person identification based on biometric data....

Mixture modelling of class-conditional densities is a standard pattern classification technique. In text classification, the use of class-conditional multinomial mixtures can be seen as a generalisation of the Naive Bayes text classifier relaxing its (class-conditional feature) independence assumption. In this paper, we describe and compare several extensions of the class-conditional multinomia...

This chapter details similarity discriminant analysis (SDA), a new framework for similaritybased classification. The two defining characteristics of the SDA classification framework are similarity-based and generative. The classifiers in this framework are similarity-based, because they classify based on the pairwise similarities of data samples, and they are generative, because they build clas...

In this paper, a classification method of four moving objects including car, people, motorcycle and bicycle in surveillance video was presented by using machine learning idea. The method can be described as three steps: feature selection, training of Support Vector Machine(SVM) classifier and performance evaluation. Firstly, a feature vector to represent the discriminabilty of an object is desc...

This paper presents a method for tracking human head in cluttered scenes to achieve robustness to occlusions and environment change by introducing inheritance and evolution concept into tracking system. Different from most works on tracking where detection and tracking are loosely coupled, we view the essence of object detection and tracking as a process of modeling object class classifier (in ...