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Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

Article discussing collaborative filtering for brain-computer interaction using transfer learning and active class selection.

Date: February 21, 2013

Creator: Wu, Dongrui; Lance, Brent J. & Parsons, Thomas D.

Item Type: Article

Partner: UNT College of Arts and Sciences

Quantile Regression Deep Q-Networks for Multi-Agent System Control

Training autonomous agents that are capable of performing their assigned job without fail is the ultimate goal of deep reinforcement learning. This thesis introduces a dueling Quantile Regression Deep Q-network, where the network learns the state value quantile function and advantage quantile function separately. With this network architecture the agent is able to learn to control simulated robots in the Gazebo simulator. Carefully crafted reward functions and state spaces must be designed for… more

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Date: May 2019

Creator: Howe, Dustin

Item Type: Thesis or Dissertation

Partner: UNT Libraries

open access

Data Quality Evaluation and Improvement for Machine Learning

In this research the focus is on data-centric AI with a specific concentration on data quality evaluation and improvement for machine learning. We first present a practical framework for data quality evaluation and improvement, using a legal domain as a case study and build a corpus for legal argument mining. We first created an initial corpus with 4,937 instances that were manually labeled. We define five data quality evaluation dimensions: comprehensiveness, correctness, variety, class imbala… more

Date: May 2022

Creator: Chen, Haihua

Item Type: Thesis or Dissertation

Partner: UNT Libraries

open access

Detection and Classification of Cancer and Other Noncommunicable Diseases Using Neural Network Models

Here, we show that training with multiple noncommunicable diseases (NCDs) is both feasible and beneficial to modeling this class of diseases. We first use data from the Cancer Genome Atlas (TCGA) to train a pan cancer model, and then characterize the information the model has learned about the cancers. In doing this we show that the model has learned concepts that are relevant to the task of cancer classification. We also test the model on datasets derived independently of the TCGA cohort and s… more

Date: July 2023

Creator: Gore, Steven Lee

Item Type: Thesis or Dissertation

Partner: UNT Libraries

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Collaborative Filtering for Brain-Computer Interaction Using Transfer Learning and Active Class Selection

Quantile Regression Deep Q-Networks for Multi-Agent System Control

Data Quality Evaluation and Improvement for Machine Learning

Detection and Classification of Cancer and Other Noncommunicable Diseases Using Neural Network Models