Face recognition has always been a challenging task in reallife surveillance videos, with partial occlusion being one of the key factors affecting the robustness of face recognition systems. Previous researches had approached the problem of face recognition with partial occlusions by dividing a face image into local patches, and training an independent classifier for each local patch. The final...

We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy. Such a taxonomy may not have been available or referenced when the individual classifiers were designed and trained, yet mapping the output labels into the taxonomy is desirable to integrate the effort spent in training the constituent classifiers. A hierarchical taxonomy representing som...

There are various machine learning algorithms for extracting patterns from data; but recently, decision combination has become popular to improve accuracy over single learner systems. The fundamental idea behind combining the decisions of an ensemble of classifiers is that different classifiers most probably misclassify different patterns and by suitably combining the decisions of complementary...

In this paper we aim to investigate the trade off in selection of an accurate, robust and costeffective classification model for binary classification problem. With empirical observation we present the evaluation of one-class and two-class classification model. We have experimented with four two-class and one-class classifier models on five UCI datasets. We have evaluated the classification mod...

In statically typed languages the set of classes and similar classifiers is commonly fully determined at compile time. Complete classifier representations can then be loaded at run-time, e.g., from a an executable file or a class file. However, some typing constructs—such as virtual classes—enable a type safe treatment of classifiers and their associated types and instances, even in the case wh...

A classification technique (or classifier) is a systematic approach used in building classification models from an input data set. Some examples include decision tree classifier, rule based classifiers, neural networks, support vector machines and naïve Bayes classifiers. Each technique employs a learning algorithm to identify a model that best fits the relationships between the attribute set a...

Feature Selection (FS) is a crucial preprocessing step in Text Classification (TC) systems. FS can be either Class-Based or Corpus-Based. Polynomial Network (PN) classifiers have proved recently to be competitive in TC using a very small subset of corpora features. This paper presents an empirical study of the performance of PN classifiers using Aggressive Class-Based FS. Seven of the stateof-t...

We present a new learning architecture: the Decision Directed Acyclic Graph (DDAG), which is used to combine many two-class classifiers into a multiclass classifier. For an -class problem, the DDAG contains classifiers, one for each pair of classes. We present a VC analysis of the case when the node classifiers are hyperplanes; the resulting bound on the test error depends on and on the margin ...

Although probabilistic networks and fuzzy clustering may seem to be disparate areas of research, they can both be seen as generalizations of naive Bayes classifiers. If all descriptive attributes are numeric, naive Bayes classifiers often assume an axis-parallel multidimensional normal distribution for each class. Probabilistic networks remove the requirement that the distributions must be axis...