Search Results for author:
Found 6 papers, 0 papers with code
Multi-Scale Label Relation Learning for Multi-Label Classification Using 1-Dimensional Convolutional Neural Networks
By training a model with multiple kernel sizes, the method is able to learn the dependency relations among labels at multiple scales, while it uses a drastically smaller number of parameters.
Detection of Abnormal Input-Output Associations
We study a novel outlier detection problem that aims to identify abnormal input-output associations in data, whose instances consist of multi-dimensional input (context) and output (responses) pairs.
Detecting Unusual Input-Output Associations in Multivariate Conditional Data
We present a novel outlier detection framework that identifies abnormal input-output associations in data with the help of a decomposable conditional probabilistic model that is learned from all data instances.
Dealing with Class Imbalance using Thresholding
In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers.
MCODE: Multivariate Conditional Outlier Detection
Outlier detection aims to identify unusual data instances that deviate from expected patterns.
A Mixtures-of-Experts Framework for Multi-Label Classification
We develop a novel probabilistic approach for multi-label classification that is based on the mixtures-of-experts architecture combined with recently introduced conditional tree-structured Bayesian networks.
