Rough set theory is an efficient tool for dealing with inexact and uncertain information.
Numerous studies have focused on rough set theory and associated methodologies, and in …

With the recent emergence of mobile platforms capable of executing increasingly complex
software and the rising ubiquity of using mobile platforms in sensitive applications such as …

Discriminative learning is challenging when examples are sets of features, and the sets vary
in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods …

Human activity recognition is a challenging task, especially when its background is unknown
or changing, and when scale or illumination differs in each video. Approaches utilizing …

We propose piecewise linear kernel-based support vector clustering (SVC) as a new
approach tailored to data-driven robust optimization. By solving a quadratic program, the …

Bag-of-features (BoF) deriving from local keypoints has recently appeared promising for
object and scene classification. Whether BoF can naturally survive the challenges such as …

We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime
and memory complexity that is independent of the number of support vectors. This class of …

In numerous domains it is useful to represent a single example by the set of the local
features or parts that comprise it. However, this representation poses a challenge to many …

In complex pattern recognition tasks, objects are typically characterized by means of
multimodality attributes, including categorical, numerical, text, image, audio, and even …

We introduce a method that enables scalable similarity search for learned metrics. Given
pairwise similarity and dissimilarity constraints between some examples, we learn a …