Browsing by Author "Vatsavai, Ranga R."
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Item A Comparative Study on Web Prefetching(2001-05-31) Bhushan Pandey, Ajay; Vatsavai, Ranga R.; Ma, Xiaobin; Srivastava, Jaideep; Shekhar, ShashiThe growth of the World Wide Web has emphasized the need for improved user latency. Increasing use of dynamic pages, frequent changes in the site structure, and user access patterns on the internet have limited the efficacy of caching techniques and emphasized the need for prefetching. Since prefecthing increses bandwidth, it is important that the prediction model is highly accurate and computationally feasible. It has been observed that in a web environment, certain sets of pages exhibit stronger correlations than others, a fact which can be used to predict future requests. Previous studies on predictive models are mainly based on pair interactions of pages and TOP-N approaches. In this paper we study a model based on page interactions of higher order where we exploit set relationships among the pages of a web site. We also compare the performance of this approach with the models based on pairwise interaction and the TOP-N approach. We have conducted a comparative study of these models on a real server log and five synthetic logs with varying page frequency distributions to simulate different real life web sites and identified dominance zones for each of these models. We find that the model based on higher order page interaction is more robust and gives competitive performance in a variety of situations.Item A Spatial Semi-supervised Learning Method for Mining Multi-spectral Remote Sensing Imagery(2004-03-01) Vatsavai, Ranga R.; Shekhar, Shashi; Burk, Thomas E.Supervised learning, which is often used in land cover (thematic) classification of remote sensing imagery, has two limitations: first these techniques require large amounts of accurate training data to accurately estimate underlying statistical model parameters and secondly, the independent and identically distributed (i.i.d) assumptions made by these techniques do not hold true in the case of high-resolution satellite images. Recently, semi-supervised learning techniques that utilize large unlabeled training samples in conjunction with small labeled training data are becoming popular in machine learning, especially in text data mining. These techniques provide a viable solution to small training dataset problems; however, the techniques do not exploit spatial context. In this paper we explore methods that utilize unlabeled samples in supervised learning for classification of multi-spectral remote sensing imagery, while also taking into account the spatial context in the learning process. We extended the classical Expectation-Maximization (EM) technique to model spatial context via Markov Random Fields (MRF). We have conducted several experiments on real data sets and our classification procedure shows an improvement of 10% in overall classification accuracy. Further studies are necessary to assess the true potential and usefulness of this technique in varying geographic settings. Keywords: MAP, MLE, EM, Spatial Context, Auto-correlation, MRF, semi-supervised learning, mixture modelsItem An approach to regional land cover classification in the Upper Great Lakes States.(University of Minnesota, 2000-07) Hansen, Sonja K.; Bolstad, Paul V.; Wilson, B. Tyler; Vatsavai, Ranga R.; Burk, Thomas E.; Bauer, Marvin E.Item Spatial Contextual Classification and Prediction Models for Mining Geospatial Data(2002-02-14) Shekhar, Shashi; Schrater, Paul; Vatsavai, Ranga R.; WuLi, Wei; Chawla, SanjayModeling spatial context (e.g., autocorrelation) is a key challenge in classification problems that arise in geospatial domains. Markov Random Fields (MRFs) is a popular model for incorporating spatial context into image segmentation and land-use classification problems. The spatial autoregression model (SAR), which is an extension of the classical regression model for incorporating spatial dependence, is popular for prediction and classification of spatial data in regional economics, natural resources, and ecological studies. There is little literature comparing these alternative approaches to facilitate the exchange of ideas (e.g., solution procedures). We argue that the SAR model makes more restrictive assumptions about the distribution of feature values and class boundaries than MRF. The relationship between SAR and MRF is analogousto the relationship between regression and Bayesian classifiers. This paper provides comparisons between the two models using a probabilistic and an experimental framework. Keywords: Spatial Context, Spatial Data Mining, Markov Random Fields, Spatial Autoregression.