This paper attempts to identify the requirement and the development of machine learning‐
based mobile big data (MBD) analysis through discussing the insights of challenges in the …

Recent studies show that deep neural networks (DNN) are vulnerable to adversarial
examples, which aim to mislead DNNs by adding perturbations with small magnitude. To …

Time series prediction, especially financial time series prediction, is a challenging task in
machine learning. In this issue, the data are usually non-stationary and volatile in nature …

摘要随着产业界数据量的爆炸式增长, 大数据概念受到越来越多的关注. 由于大数据的海量,
复杂多样, 变化快的特性, 对于大数据环境下的应用问题, 传统的在小数据上的机器学**算法很多 …

The fuzzy dependency function proposed in the fuzzy rough set model is widely employed in
feature evaluation and attribute reduction. It is shown that this function is not robust to noisy …

There exist uncertain data in the real world due to some factors such as imprecise
measurements and noise. Unlike deterministic data, the features of samples in uncertain …

This paper provides a sparse learning algorithm for Support Vector Classification (SVC),
called Sparse Support Vector Classification (SSVC), which leads to sparse solutions by …

Kernel methods have been successfully applied in various applications. To succeed in these
applications, it is crucial to learn a good kernel representation, whose objective is to reveal …

Fuzzy rough sets, generalized from Pawlak's rough sets, were introduced for dealing with
continuous or fuzzy data. This model has been widely discussed and applied these years. It …

In the recent years, many research methodologies are proposed to recognize the spoken
language and translate them to text. In this paper, we propose a novel iterative clustering …