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Using a set of binary classifiers to solve multiclass classification problems has been a popular approach over the years. The decision boundaries learnt by binary classifiers (also called base classifiers) are much simpler than those learnt by multiclass classifiers. This paper proposes a new classification framework, termed binarization with boosting and(More)
Using a set of binary classifiers to solve the multiclass classification problem has been a popular approach over the years. This technique is known as binarization. The decision boundary that these binary classifiers (also called base classifiers) have to learn is much simpler than the decision boundary of a multiclass classifier. But binarization gives(More)
This paper proposes a distributed peer-to-peer data lookup technique on DHTs in order to serve range queries over multiple attributes. The scheme, MARQUES, uses space filling curves to map multi-attribute data points to a one-dimensional key space and thus effectively converts multi-attribute range queries into a consecutive series of one-dimensional keys.(More)
Linear classifiers, even though very simple, are popular for classification tasks. By nature they can only differentiate between two classes. But it is possible to extend their usage into the domain of multi-class problems. These classifiers are known to perfrom very well for many practical scenarios (both binary and multi-class). In this paper, we examine(More)
Faculty members are often busy with various research work and thus have little time to prepare lectures. Advances in technology offer a solution to this problem. That is, they can record the lectures and reuse them over semesters. It is common for these lecture videos to include an inset frame of the instructor giving the lecture. However, little research(More)
We present a new computational method for the solution of elliptic eigenvalue problems with variable coefficients in general two-dimensional domains. The proposed approach is based on use of the novel Fourier continuation method (which enables fast and highly accurate Fourier approximation of nonperiodic functions in equispaced grids without the limitations(More)
Consider a teacher designing a good lecture for students, or a hacker drafting a poisonous text input against Tay the chatterbot. Both cases can be formulated as a task of constructing special training data, such that a known learning algorithm taking the constructed data will arrive at a prespecified target model. This task is known as optimal teaching,(More)
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