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Catalog / nagsubhadeep / Transfer-Adaboost
Public GitHub Catalog Discovered Sep 27, 2026

nagsubhadeep / Transfer-Adaboost

A TransferBoost algorithm is designed via the AdaBoost algorithm and implementing sequential minimal optimization algorithm for training a support vector classifier using polynomial or RBF kernels

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Programming a Feature Selection based Dynamic Transfer Ensemble Model implementing Recursive Feature Elimination Support Vector Machines. Using this model, implementing how Transfer Machine Learning can be efficiently used to process customer data in both source and target domains applied on a Customer Churn Prediction Data-Set Tools/Languages/OS Used: JAVA, LibSVM toolkit, JAVA-ML toolkit, Eclipse IDE, Weka toolkit, Ubuntu 11.10

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In this project, An Intelligent system aimed at solving the 21st century crisis of energy conservation. This system reduces human effort and also saves time and money. This scheme monitors the efficient use of electricity and hence conserves the energy which is the need of the hour. Although the prospect of computerized homes has a long history, home automation has never become terribly popular because the benefits are seldom seen to outweigh the costs. One significant cost of an automated home is that someone has to program it to behave in an adaptive manner. We describe a system that automates the ambient illumination in a room. We have noticed that hardly any attention is paid to the light entering the room from outside. Our system varies the intensity of ambient light in the room based on the amount of light entering it. We also have occupancy sensors includes entry/exit sensors for detecting movement through doorways. The system is built in such a way that over the period of time it recognizes the pattern of usage of the residents and adapts accordingly. We have built a prototype system that works on a single room. Further improvements are suggested for the scope of future work.

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