Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...

In this paper, it is aimed to investigate the capabilities of boosting classification approach for forest fire detection using SPOT-4 imagery. The study area, Bodrum in the province of Muğla, is located at the south-western Mediterranean coast of Turkey where recent largest forest fires occurred in July 2007. Boosting method is one of the recent advanced classifiers proposed in the machine lear...

The ECOC framework provides a powerful and popular method for solving multiclass problems using a multitude of binary classifiers. We had recently introduced the Binary Hierarchical Classifier (BHC) architecture that addresses multiclass classification problems using a set of binary classifiers arranged as a tree. Unlike ECOCs, the BHC groups classes according to their natural affinities in ord...

Recent advances in the study of voting classification algorithms have brought empirical and theoretical results clearly showing the discrimination power of ensemble classifiers. It has been previously argued that the search of this classification power in the design of the algorithms has marginalized the need to obtain interpretable classifiers. Therefore, the question of whether one might have...

This study aims to examine when and how readers make use of top-down information to predict or integrate upcoming words by utilizing the characteristics of Chinese classifier-noun agreement, as measured by event-related potentials (ERPs). Constraint strength of classifiers (strong and weak) and cloze probability of the pairing noun (high, low, implausible) was manipulated. Weakly constrained cl...

This manuscript studies statistical properties of linear classifiers obtained through minimization of an unregularized convex risk over a finite sample. Although the results are explicitly finite-dimensional, inputs may be passed through feature maps; in this way, in addition to treating the consistency of logistic regression, this analysis also handles boosting over a finite weak learning clas...

Designing effective and efficient classifier for pattern analysis is a key problem in machine learning and computer vision. Many the solutions to the problem require to perform logic operations such as ‘and’, ‘or’, and ‘not’. Classification and regression tree (CART) include these operations explicitly. Other methods such as neural networks, SVM, and boosting learn/compute a weighted sum on fea...

Techniques for detecting pedestrian in still images have attracted considerable research interests due to its wide applications such as video surveillance and intelligent transportation systems. In this paper, we propose a novel simpler pedestrian detector using state-of-the-art locally extracted features, namely, covariance features. Covariance features were originally proposed in [1,2]. Unlik...

We prove a multiclass boosting theory for the ResNet architectures which simultaneously creates a new technique for multiclass boosting and provides a new algorithm for ResNet-style architectures. Our proposed training algorithm, BoostResNet, is particularly suitable in non-differentiable architectures. Our method only requires the relatively inexpensive sequential training of T “shallow ResNet...

If one has a multiclass classification problem and wants to boost a multiclass base classifier AdaBoost.M1 is a well known and widely applicated boosting algorithm. However AdaBoost.M1 does not work, if the base classifier is too weak. We show, that with a modification of only one line of AdaBoost.M1 one can make it usable for weak base classifiers, too. The resulting classifier AdaBoost.M1W is...