G
George Almpanidis
Researcher at Henan University
Publications - 18
Citations - 647
George Almpanidis is an academic researcher from Henan University. The author has contributed to research in topics: Bayesian information criterion & Gamma distribution. The author has an hindex of 9, co-authored 15 publications receiving 494 citations. Previous affiliations of George Almpanidis include Aristotle University of Thessaloniki.
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An up-to-date comparison of state-of-the-art classification algorithms
TL;DR: It is found that Stochastic Gradient Boosting Trees (GBDT) matches or exceeds the prediction performance of Support Vector Machines and Random Forests, while being the fastest algorithm in terms of prediction efficiency.
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Combining text and link analysis for focused crawling-An application for vertical search engines
TL;DR: A latent semantic indexing classifier that combines link analysis with text content in order to retrieve and index domain-specific web documents and is compared with other well-known web information retrieval techniques.
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Phonemic segmentation using the generalised Gamma distribution and small sample Bayesian information criterion
TL;DR: This work presents a text-independent automatic phone segmentation algorithm based on the Bayesian Information Criterion, and uses a computationally inexpensive maximum likelihood approach for parameter estimation to evaluate the efficiency and demonstrate that the proposed adjustments yield significant performance improvement in noisy environments.
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Language identification in web documents using discrete HMMs
TL;DR: The proposed system is built on hidden Markov models that enable the modeling of character sequences that provide the means for language tracking, that is, language identification across the segments of a multilingual document.
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On Incremental Learning for Gradient Boosting Decision Trees
TL;DR: iGBDT is a novel algorithm that incrementally updates the classification model built upon gradient boosting decision tree (GBDT), namely iGBDT, to incrementally learn a new model but without running GBDT from scratch, when new data is dynamically arriving in batch.