The goal of regression and classification methods in supervised learning is to minimize the
empirical risk, that is, the expectation of some loss function quantifying the prediction error …

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose
a subset of relevant samples from the input data and make reliable probabilistic predictions …

In this paper, a novel robust fault detection (FD) approach called parity space vector
machine (PSVM) is proposed for linear discrete-time systems. Aiming to achieve a tradeoff …

A quality-related process monitoring method based on parametric t-distributed stochastic
neighbour embedding (Pt-SNE) and modified minimum error minimax probability machine …

Near-infrared (NIR) spectroscopy plays a critical role in online analysis of difficult-to-
measure properties of petrochemicals. In industrial applications, a calibration model among …

In this paper, we propose novel second-order cone programming formulations for binary
classification, by extending the Minimax Probability Machine (MPM) approach. Inspired by …

Deep neural networks enjoy a powerful representation and have proven effective in a
number of applications. However, recent advances show that deep neural networks are …

Sažetak We propose a novel two-party privacy-preserving classification solution called
Collaborative Classification Mechanism for Privacy-preserving (C2MP2) over horizontally …

Machine learning methods have had considerable success across a wide range of
applications. Much of this success is due to the flexibility of learning algorithms and their …

Background: According to the characteristics of phase noise, phase noise power spectrum
feature was used for emitter individual identification. Methods: For different emitter …