proper forest management needs quantitative and precise estimates of forest stands characteristics. remotely sensed imageries, due to accurate and broad spatial information, has become a cost-effective tool in forest management. classification of forest attributes and generation of thematic maps are among the common applications of remote sensing. the objective of this study was to optimize the...

weather data generators (wgs) have been developed for an extension of time series of such weather variables as rainfall, temperature and relative humidity to provide better understanding of systems affected by climatic factors. different algorithms have been applied in these generators, broadly divided into parametric & non-parametric ones. in this study, the performance of non-parametric gener...

In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leave-one-out KNN score on the training set. It can also learn a low-dimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our...

KNN algorithm is a simple, effective, non-parametric classification, and has been widely used in text classification, pattern recognition, image and spatial classification. Research on improvements about KNN algorithm has broad application prospects and important scientific significance. Based on analysis about classic KNN and its improved algorithms, we find its over-reliance on the choice of ...

We develop an optical character recognition (OCR) engine for handwritten Sanskrit using a two-stage classifier. Inside the standard OCR pipeline, we focus on the classification problem assuming characters have been preprocessed decently. One challenge we face is that the language of Sanskrit has about a hundred core characters where model driven methods, like Support Vector Machine (SVM), have ...