According to the defects of KNN(K-Nearest Neighbor) algorithm and SVM(Support Vector Machine) algorithm in tracking a moving target such the large consumption and the low accuracy of target tracking error, a tracking model of moving target is proposed based on the combination of KNN algorithm and SVM algorithm with minimum distance optimization. First categories divided according to the princip...

Support vector machine (SVM) is one of the most powerful supervised learning algorithms in gene expression analysis. The samples intermixed in another class or in the overlapped boundary region may cause the decision boundary too complex and may be harmful to improve the precise of SVM. In the present paper, hybridized k-nearest neighbor (KNN) classifiers and SVM (HKNNSVM) is proposed to deal w...

The video retrieval system we developed for TRECVID 2012 mainly involves the semantic indexing task which includes key frame extraction, low level feature extraction, classification and concept fusion. We extracted a new low level feature, explored various classification and fusion schemes. Four “light” runs and two 2 “pair” runs we submitted are as follows: L_A_FudaSys1: Fusion based on concep...

Die Anforderungen an die moderne Landwirtschaft bedingen einen effizienten Ressourceneinsatz, auch bei der N-Düngung. Ökonomisch optimierte Entscheidungsregeln wurden mit Künstlichen Neuronalen Netzen (KNN) und Support-Vector-Machines (SVM) erzeugt und in der Anwendung mit einer betriebseinheitlichen Variante verglichen. Es zeigte sich, dass Einsparpotentiale von bis zu 69 kg N/ha möglich sind ...

We consider improving the performance of k-Nearest Neighbor classifiers. A regularized kNN is proposed to learn an optimal dissimilarity function to substitute the Euclidean metric. The learning process employs hyperkernels and shares a similar regularization framework as support vector machines (SVM). Its performance is shown to be consistently better than kNN, and is competitive with SVM.

In supervised learning problems, global and local learning algorithms are used. In contrast to global learning algorithms, the prediction of a local learning algorithm in a testing point is only based on training data which are close to the testing point. Every global algorithm such as support vector machines (SVM) can be localized in the following way: in every testing point, the (global) lear...

Data Mining has great scope in the field of medicine. In this article we introduced one new fuzzy approach for prediction of hepatitis disease. Many researchers have proposed the use of K-nearest neighbor (KNN) for diabetes disease prediction. Some have proposed a different approach by using K-means clustering for reprocessing and then using KNN for classification. In our approach Naive Bayes c...

Heterogeneous features of thyroid nodules in ultrasound images is very difficult task when radiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In...