no code implementations • 5 Oct 2020 • Behzad Ghazanfari, Fatemeh Afghah
This paper introduces Multi-Level feature learning alongside the Embedding layer of Convolutional Autoencoder (CAE-MLE) as a novel approach in deep clustering.
no code implementations • 26 Sep 2020 • Behzad Ghazanfari, Fatemeh Afghah, Sixian Zhang
To evaluate the performance of this method in time series analysis, we applied the proposed layer in two publicly available datasets of PhysioNet competitions in 2015 and 2017 where the input data is ECG signal.
no code implementations • 9 Mar 2020 • Behzad Ghazanfari, Fatemeh Afghah
This paper introduces a novel perspective about error in machine learning and proposes inverse feature learning (IFL) as a representation learning approach that learns a set of high-level features based on the representation of error for classification or clustering purposes.
no code implementations • 8 Mar 2020 • Behzad Ghazanfari, Fatemeh Afghah, Mohammadtaghi Hajiaghayi
This paper proposes inverse feature learning as a novel supervised feature learning technique that learns a set of high-level features for classification based on an error representation approach.
no code implementations • 17 Apr 2019 • Behzad Ghazanfari, Fatemeh Afghah, Kayvan Najarian, Sajad Mousavi, Jonathan Gryak, James Todd
In this paper, we propose a novel set of high-level features based on unsupervised feature learning technique in order to effectively capture the characteristics of different arrhythmia in electrocardiogram (ECG) signal and differentiate them from irregularity in signals due to different sources of signal disturbances.
no code implementations • 17 Nov 2018 • Behzad Ghazanfari, Fatemeh Afghah, Matthew E. Taylor
Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces.
no code implementations • 14 Sep 2017 • Behzad Ghazanfari, Matthew E. Taylor
This paper proposes a novel practical method that can autonomously decompose tasks, by leveraging association rule mining, which discovers hidden relationship among entities in data mining.