Browsing by Author "Delibalta, İ."
Now showing items 1-11 of 11
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Adaptive hierarchical space partitioning for online classification
(IEEE, 2016)We propose an online algorithm for supervised learning with strong performance guarantees under the empirical zero-one loss. The proposed method adaptively partitions the feature space in a hierarchical manner and generates ... -
Big data signal processing using boosted RLS algorithm
(IEEE, 2016)We propose an efficient method for the high dimensional data regression. To this end, we use a least mean squares (LMS) filter followed by a recursive least squares (RLS) filter and combine them via boosting notion extensively ... -
Boosted LMS-based piecewise linear adaptive filters
(IEEE, 2016)We introduce the boosting notion extensively used in different machine learning applications to adaptive signal processing literature and implement several different adaptive filtering algorithms. In this framework, we ... -
Mathematical model of causal inference in social networks
(IEEE, 2016)In this article, we model the effects of machine learning algorithms on different Social Network users by using a causal inference framework, making estimation about the underlying system and design systems to control ... -
Mixture of set membership filters approach for big data signal processing
(IEEE, 2016)In this work, we propose a new approach for mixture of adaptive filters based on set-membership filters (SMF) which is specifically designated for big data signal processing applications. By using this approach, we achieve ... -
Nonlinear regression using second order methods
(IEEE, 2016)We present a highly efficient algorithm for the online nonlinear regression problem. We process only the currently available data and do not reuse it, hence, there is no need for storage. For the nonlinear regression, we ... -
Online adaptive hierarchical space partitioning classifier
(IEEE, 2016)We introduce an on-line classification algorithm based on the hierarchical partitioning of the feature space which provides a powerful performance under the defined empirical loss. The algorithm adaptively partitions the ... -
Online churn detection on high dimensional cellular data using adaptive hierarchical trees
(IEEE, 2016)We study online sequential logistic regression for churn detection in cellular networks when the feature vectors lie in a high dimensional space on a time varying manifold. We escape the curse of dimensionality by tracking ... -
Online text classification for real life tweet analysis
(IEEE, 2016)In this paper, we study multi-class classification of tweets, where we introduce highly efficient dimensionality reduction techniques suitable for online processing of high dimensional feature vectors generated from ... -
Piecewise linear regression based on adaptive tree structure using second order methods
(IEEE, 2016)We introduce a highly efficient online nonlinear regression algorithm. We process the data in a truly online manner such that no storage is needed, i.e., the data is discarded after used. For nonlinear modeling we use a ... -
A tree-based solution to nonlinear regression problem
(IEEE, 2016)In this paper, we offer and examine a new algorithm for sequential nonlinear regression problem. In this architecture, we use piecewise adaptive linear functions to find the nonlinear regression model sequentially. For ...
