Zusammenfassung We evaluate the performance of Import Vector Machines (IVM), a sparse Kernel Logistic Regression approach, for the classification of hyperspectral data. The IVM classifier is applied on two different data sets, using different number of training samples. The performance of IVM to Support Vector Machines (SVM) is compared in terms of accuracy and sparsity. Moreover, the impact of...

Small and medium-sized logistics companies are playing an increasingly important part in social life. We selected the data of small and medium-sized logistical companies in Beijing, Shanghai and Guangzhou, reformulated ordinal regression support vector machine method so that different input points could make different contributions to decide hyper plane, to analyze multi-classification of credi...

In this work, we developed classifiers to distinguish between four ovarian tumor types using Bayesian least squares support vector machines (LS-SVMs) and kernel logistic regression. Input selection using rank-one updates for LS-SVMs performed better than automatic relevance determination. Evaluation on an independent test set showed good performance of the classifiers to distinguish between all...

We present a new method that compresses sentences by removing words. In a first stage, it generates candidate compressions by removing branches from the source sentence’s dependency tree using a Maximum Entropy classifier. In a second stage, it chooses the best among the candidate compressions using a Support Vector Machine Regression model. Experimental results show that our method achieves st...

In this article we study the effects of introducing structure in the input distribution of the data to be learnt by a simple perceptron. We determine the learning curves within the framework of Statistical Mechanics. Stepwise generalization occurs as a function of the number of examples when the distribution of patterns is highly anisotropic. Although extremely simple, the model seems to captur...

Abstract Rainfall prediction has a major effect on human civilization and is one of the most difficult, unpredictable activities. Precise accurate predictions will help to rising financial risks pro-actively. This work presents current supervised learning models machine focused Prediction. also significant issue in planet because it impacts any single aspects that relies being. Unpredictable re...

in the last two decade the use of aerial laser scanner (als) or lidar (light detection and ranging) sensor in geomatics engineering and surveying application has augmented significantly . the main reason of the mentioned phenomenon is the reliability and accuracy of the data obtained by lidar sensors. the output of lidar is unclassified 3d point cloud. classification of the lidar point clouds i...

classification ensemble, which uses the weighed polling of outputs, is the art of combining a set of basic classifiers for generating high-performance, robust and more stable results. this study aims to improve the results of identifying the persian handwritten letters using error correcting output coding (ecoc) ensemble method. furthermore, the feature selection is used to reduce the costs of ...

There are several methods used for the classification problems. many different kinds of fields that can be used. Nowadays, Support Vector Machine (SVM) is a popular method has been proposed by researchers. Using same but distribution creating training and testing data in dataset yield varying results terms prediction accuracy, which crucial classification. In this paper, we compare accuracy bet...