In this article, a novel approach using ensemble of semi-supervised classifiers is proposed for change detection in remotely sensed images. Unlike the other traditional methodologies for detection of changes in land-cover, the present work uses a multiple classifier system in semi-supervised (leaning) framework instead of using a single weak classifier. Iterative learning of base classifiers is...

Recent findings in the domain of combining classifiers provide a surprising revision of the usefulness of diversity for modelling combined performance. Although there is a common agreement that a successful fusion system should be composed of accurate and diverse classifiers, experimental results show very weak correlations between various diversity measures and combining methods. Effectively n...

We propose a novel approach to boosting weighted linear discriminant analysis (LDA) as a weak classifier. Combining Adaboost with LDA allows to select the most relevant features for classification at each boosting iteration, thus benefiting from feature correlation. The advantages of this approach include the use of a smaller number of weak learners to achieve a low error rate, improved classif...

Online learning for object detection is an important requirement for many computer vision applications. In this paper, we present an iterative optimization algorithm that learns separable linear classifiers from a sample of positive and negative example images. We demonstrate that separability not only leads to rapid runtime behavior but enables very fast training. Experimental results underlin...

We adapted a visual object detection framework, originally applied to face recognition, to perform fast and robust door handle detection. We detect objects in real time using a cascade of weak classifiers. The framework rejects most image subwindows early on in the cascade and a very small minority ever reaches the later nodes. Our system achieves a high detection rate (approximately 95%) while...

In this paper we investigate Named Entity Recognition (NER) systems using two well-known classifiers in the machine learning literature: Markov Models and Decision Trees. We have designed several systems to check the impact of introducing different characteristics which have a weak dependence of the language used. We also report the results obtained by our systems on the Spanish corpus provided...

In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good result...

this article investigates the syntactic structure of numeral classifiers in persian dps within the minimalist program. numeral classifiers are morphemes by which nouns are numerated by a number category. the morpho-syntactic analysis of classifiers in comparison to the other constituents of dps like number, based on cheng and sybesma (2005), ishii (2000), li(1998, 1999), tang (2004), simpson (2...

objective(s): this study addresses feature selection for breast cancer diagnosis. the present process uses a wrapper approach using ga-based on feature selection and ps-classifier. the results of experiment show that the proposed model is comparable to the other models on wisconsin breast cancer datasets. materials and methods: to evaluate effectiveness of proposed feature selection method, we ...

For medical classification problems, it is often desirable to have a probability associated with each class. Probabilistic classifiers have received relatively little attention for small n large p classification problems despite of their importance in medical decision making. In this paper, we introduce 2 criteria for assessment of probabilistic classifiers: well-calibratedness and refinement a...