Distance Sensitive AdaBoost using Distance Weight Function

یادگیری نیمه نظارتی کرنل مرکب با استفاده از تکنیک‌های یادگیری معیار فاصله

Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...

Derivation Of Shadow Price For CO2 Gas Emission Using Distance Function Approach

Can Boosting with SVM as Week Learners Help?

Object recognition in images involves identifying objects with partial occlusions, viewpoint changes, varying illumination, cluttered backgrounds. Recent work in object recognition uses machine learning techniques SVM-KNN, Local Ensemble Kernel Learning, Multiple Kernel Learning. In this paper, we want to utilize SVM as week learners in AdaBoost. Experiments are done with classifiers like neare...

Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier

In this work we introduce a new distance estimation technique by boosting and we apply it to the K-Nearest Neighbor Classifier (KNN). Instead of applying AdaBoost to a typical classification problem, we use it for learning a distance function and the resulting distance is used into K-NN. The proposed method (Boosted Distance with Nearest Neighbor) outperforms the AdaBoost classifier when the tr...

Investigating factors affecting the occurrence of mass movements using Multivariate Linear Regression (Case study: Abidar watershed)

Investigating the factors affecting the occurrence of mass movements, help to identify sensitive areas and providing solutions and ways to control and proper management, partly to prevent the occurrence of mass movements. This research tried to recognize the factor affecting the occurrence of mass movements in the Forest Park Abidar watershed. In this research, information layers including lith...