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Data Clustering: Algorithms and Applications
TLDR
Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. Expand
A survey on platforms for big data analytics
TLDR
This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. Expand
UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization
TLDR
We propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Expand
Short-Text Topic Modeling via Non-negative Matrix Factorization Enriched with Local Word-Context Correlations
TLDR
In this paper, we propose a semantics-assisted non-negative matrix factorization (SeaNMF) model to discover topics for the short texts. Expand
A Multi-Task Learning Formulation for Survival Analysis
TLDR
We reformulate the survival analysis problem as a multi task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration. Expand
Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization
TLDR
This paper presents a novel topic modeling method based on joint nonnegative matrix factorization, which simultaneously discovers common as well as discriminative topics given multiple document sets. Expand
Adaptive Boosting for Transfer Learning Using Dynamic Updates
TLDR
We incorporate a dynamic factor into TrAdaBoost to make it meet its intended design of incorporating the advantages of both AdaBoost and the "Weighted Majority Algorithm". Expand
Machine Learning for Survival Analysis: A Survey
TLDR
We provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. Expand
Machine Learning for Survival Analysis
TLDR
Survival analysis is a subfield of statistics where the goal is to analyze and model data where the outcome is the time until an event of interest occurs. Expand
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
TLDR
We present an empirical study of two variants of boosting algorithms - AdaBoost and LogitBoost for the problem of fold recognition. Expand
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