A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Efficient Skin Segmentation via Neural Networks: HP-ELM and BD-SOM

AbstractThis paper presents two novel methods for skin detection: HP-ELM and BD-SOM. Both SOM and ELM are fast for large data sets, but not yet suitable for Big Data. We show how they can be improved in order to fulfill the strict requirements for Big Data. Both new methods are described and their implementations are explained. A comparison on a large example is presented in the experiment section. We find that BD-SOM is more accurate but not as computationally efficient as HP-ELM. As a result, we show that both methods work well on a Big Data task. The given task deals with the classification of more than one billion samples (pixels) between Skin and Non Skin categories

Mislabel Detection of Finnish Publication Ranks

The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM). The purpose is to introduce and test recently proposed ELM-based mislabel detection approach with a rich set of features characterizing a publication channel. We will compare the architecture, accuracy, and, especially, the set of detected mislabels of the ELM-based approach to the corresponding reference results in.peerReviewe

Extreme Learning Machines for VISualization+R: Mastering Visualization with Target Variables

The Current Paper Presents an Improvement of the Extreme Learning Machines for Visualization (Elmvis+) Nonlinear Dimensionality Reduction Method. in This Improved Method, Called Elmvis+r, It is Proposed to Apply the Originally Unsupervised Elmvis+ Method for the Regression Problems, using Target Values to Improve Visualization Results. It Has Been Shown in Previous Work that the Approach of Adding Supervised Component for Classification Problems Indeed Allows to Obtain Better Visualization Results. to Verify This Assumption for Regression Problems, a Set of Experiments on Several Different Datasets Was Performed. the Newly Proposed Method Was Compared to the Elmvis+ Method And, in Most Cases, Outperformed the Original Algorithm. Results, Presented in This Article, Prove the General Idea that using Supervised Components (Target Values) with Nonlinear Dimensionality Reduction Method Like Elmvis+ Can Improve Both Visual Properties and overall Accuracy

Comparing ELM with SVM in the field of sentiment classification of social media text data

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