Lazy classifiers store all of the training samples and do not build a classifier until a new sample needs to be classified. It differs from eager classifiers, such as decision tree induction, which build a general model (such as a decision tree) before receiving new samples. K-nearest neighbor (KNN) classification is a typical lazy classifier. Given a set of training data, a knearest neighbor c...

Ensemble learning has attracted considerable attention owing to its good generalization performance. The main issues in constructing a powerful ensemble include training a set of diverse and accurate base classifiers, and effectively combining them. Ensemble margin, computed as the difference of the vote numbers received by the correct class and the another class received with the most votes, i...

Recent researches have shown that ensembles of classifiers have more accuracy than a single classifier. Baging, boosting and error correcting output codes (ECOC) are most common ways for creating combination of classifiers. In this paper a new method for ensemble of classifiers has been introduced and performance of this method examined by applying to handwritten pen digits dataset. Experimenta...

Class noise usually means the erroneous labeling of the training examples. In pattern recognition problems, class noise occurs frequently and deteriorates the classifier derived from the noisy dataset. For instance, in some adaptive image segmentation system, the class labels of the training pixels are assigned automatically and sometimes contain errors. Since image segmentation plays a crucial...

Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit the fact that each example is only assigned to one class. Additional problems with extending semisupervised binary classifiers to multi-class p...

We present a two-class pattern recognition method through the majority vote which is based on weak classifiers. The weak classifiers are defined‘in terms of rectangular regions formed by the original training data. Tests on real and simulated data sets show that this classifier combination procedure can lead to a high accuracy.

In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement ...

In many real-world tasks of image classification, limited amounts of labeled data are available to train automatic classifiers. Consequently, extensive human expert involvement is required for verification. A novel solution is presented that makes use of active learning combined with an ensemble of classifiers for each class. The result is a significant reduction in required expert involvement ...

One-class classifiers employing for training only the data from one class are justified when the data from other classes is difficult to obtain. In particular, their use is justified in mobile-masquerader detection, where user characteristics are classified as belonging to the legitimate user class or to the impostor class, and where collecting the data originated from impostors is problematic....