258 citations · 628 across the 13 of their papers we have counts for
8 papers · 1 filter
Adaptive Signal Detection and Parameter Estimation in Unknown Colored Gaussian Noise
Bo Tang, Haibo He, Steven Kay
This paper considers the general signal detection and parameter estimation problem in the presence of colored Gaussian noise disturbance. By modeling the disturbance with an autore…
Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise
Bo Tang, Jun Yan, Steven Kay +1
In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By model…
A Local Density-Based Approach for Local Outlier Detection
Bo Tang, Haibo He
This paper presents a simple but effective density-based outlier detection approach with the local kernel density estimation (KDE). A Relative Density-based Outlier Score (RDOS) is…
Probabilistic Human Mobility Model in Indoor Environment
Bo Tang, Chao Jiang, Haibo He +1
Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot…
Kernel-based Generative Learning in Distortion Feature Space
Bo Tang, Paul M. Baggenstoss, Haibo He
This paper presents a novel kernel-based generative classifier which is defined in a distortion subspace using polynomial series expansion, named Kernel-Distortion (KD) classifier.…
FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization
Bo Tang, Haibo He
In this paper, we present a new wrapper feature selection approach based on Jensen-Shannon (JS) divergence, termed feature selection with maximum JS-divergence (FSMJ), for text cat…
EEF: Exponentially Embedded Families with Class-Specific Features for Classification
Bo Tang, Steven Kay, Haibo He +1
In this letter, we present a novel exponentially embedded families (EEF) based classification method, in which the probability density function (PDF) on raw data is estimated from…
Toward Optimal Feature Selection in Naive Bayes for Text Categorization
Bo Tang, Steven Kay, Haibo He
Automated feature selection is important for text categorization to reduce the feature size and to speed up the learning process of classifiers. In this paper, we present a novel a…