Rapid developments in hardware, software, and communication technologies have
facilitated the emergence of Internet-connected sensory devices that provide observations …

Linear classification is a useful tool in machine learning and data mining. For some data in a
rich dimensional space, the performance (ie, testing accuracy) of linear classifiers has …

Online algorithms that process one example at a time are advantageous when dealing with
very large data or with data streams. Stochastic Gradient Descent (SGD) is such an …

The increasing use of statistical data analysis in enterprise applications has created an arms
race among database vendors to offer ever more sophisticated in-database analytics. One …

Abstract Kernel Support Vector Machine delivers state-of-the-art results in non-linear
classification, but the need to maintain a large number of support vectors poses a challenge …

Recent advances in linear classification have shown that for applications such as document
classification, the training process can be extremely efficient. However, most of the existing …

Kernel trick is widely applied to Support Vector Machine (SVM) to deal with linearly
inseparable data which is known as kernel SVM. However, kernel SVM always has high …

Nowadays biomedical research is developing rapidly. A large number of biomedical
knowledge exists in the form of unstructured text documents in various files. Named Entity …

Aero-engine aerodynamic model is widely applied to identify the aerodynamic parameters of
components like compressor pressure, turbine temperature and so on. A data-driven …

The immense amount of data created by digitalization requires parallel computing for
machine-learning methods. While there are many parallel implementations for support …