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Big data comes in various ways, types, shapes, forms and sizes.
Indeed, almost all areas of science, technology, medicine, public health,
economics, business, linguistics and social science are bombarded by ever
increasing flows of data begging to be analyzed efficiently and effectively. In
this paper, we propose a rough idea of a possible taxonomy of big data,
along with some of the most commonly used tools for handling each particular
category of bigness. The dimensionality p of the input space...
It is well established that accent recognition can be as accurate
as up to 95% when the signals are noise-free, using feature extraction
techniques such as mel-frequency cepstral coefficients and binary classifiers such
as discriminant analysis, support vector machine and k-nearest neighbors. In
this paper, we demonstrate that the predictive performance can be reduced
by as much as 15% when the signals are noisy. Specifically, in this paper we
perturb the signals with different levels of white...
This research evaluates pattern recognition techniques on a subclass of big data
where the dimensionality of the input space (p) is much larger than the number of
observations (n). Specifically, we evaluate massive gene expression microarray cancer data
where the ratio κ is less than one. We explore the statistical and computational challenges
inherent in these high dimensional low sample size (HDLSS) problems and present
statistical machine learning methods used to tackle and circumvent these difficulties.
Regularization...
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