For the operational application of multi-temporal ENVISAT ASAR APS data to rice mapping, a complex Wishart distribution based multi-temporal classifier was evaluated in this paper. The classification accuracy of this classifier was quantitatively compared with commonly used classifiers for optical remote sensing image classification including maximum likelihood classifier and minimum euclidean ...

a new parametric model was developed for predicting cut point of hydraulic classifiers. the model directly uses operating parameters including pulp flowrate, feed particle size characteristics, pulp solids content, solid density and particles retention time in the classification chamber and also covers uncontrollable errors using calibration constants. the model applicability was first verified...

Textual sentiment classifiers classify texts into a fixed number of affective classes, such as positive, negative or neutral sentiment, or subjective versus objective information. It has been observed that sentiment classifiers suffer from a lack of generalization capability: a classifier trained on a certain domain generally performs worse on data from another domain. This phenomenon has been ...

In this paper, we propose a semantic naïve Bayes classifier (SNBC) to improve the conventional naïve Bayes classifier (NBC) by incorporating “document-level” semantic information for document classification (DC). To capture the semantic information from each document, we develop semantic feature extraction and modeling algorithms. For semantic feature extraction, we first apply a log-Bilinear d...

Mammography is currently the most effective imaging modality for early detection of breast cancer. In a CAD system for masses based on mammography, a mammogram is segmented to detect the masses. The segmentation gives rise to mass regions of interested (ROIs), which are either benign or malignant. There is a need to classify the extracted mass ROIs into benign and malignant masses; it is a hard...

The conformity framework has recently been proposed for the task of reliable classification. Given a classifier B, the framework allows to obtain p-values of the classifications assigned to individual instances. However, applying the framework is a difficult problem: we need to construct an instance non-conformity function for the classifier B. To avoid constructing such a function we propose a...

Where active learning with uncertainty sampling is used to generate training sets for classification applications, it is sensible to use the same type of classifier to select the most informative training examples as the type of classifier that will be used in the final classification application. There are scenarios, however, where this might not be possible, for example due to computational c...

Today world the brain tumor is life threatening and the main reason for the death. The growth of abnormal cells in brain leads to brain tumor. Brain tumor is categorized into malignant tumor and benign tumor. Malignant is cancerous whereas Benign tumor is non-cancerous. Diagnosing at earlier stage can save the person. It is actually a great challenge to find the brain tumor and classifying its ...

Although some image features and algorithms succeed in many tasks such as scene recognition and face recognition, carefully choosing image features and classifiers are time consuming for a specific image classification task. In this paper, we propose a method that automatically combines the classifiers with probability outputs from different features. We fomulate the problem in quadric programm...