LPAIA 2022
Special Session on Learning patterns/methods in current AI applications
at the 14th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2022)Ho Chi Minh City, Vietnam
Conference website: http://www.aciids.pwr.edu.pl
Special Session Organizers
Prof. dr hab. Urszula Boryczka
Institute of Computer Science
University of Silesia, Poland
E-mail: urszula.boryczka @us.edu.pl
Prof. dr hab. Piotr Porwik
Institute of Computer Science
University of Silesia, Poland
E-mail: piotr.porwik@us.edu.pl
Institute of Computer Science
University of Silesia, Poland
E-mail: urszula.boryczka @us.edu.pl
Prof. dr hab. Piotr Porwik
Institute of Computer Science
University of Silesia, Poland
E-mail: piotr.porwik@us.edu.pl
Objectives and topics
Machine learning is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. The goal of AI is to create computer models that exhibit „intelligent behaviors” like humans. This means machines that can recognize a visual scene, understand a text written in natural language, or perform an action in the physical world in an optimal manner. Machine learning is one way to use AI. It was defined in the 1950s as the field of study that gives computers the ability to learn without explicitly being programmed.
Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data.
As such, there are many different types of learning that we may encounter as a practitioners in the field of machine learning: from whole fields of study to specific techniques. We want to focus on fields of study, such as supervised, unsupervised, and reinforcement learning, hybrid types of learning, such as semi-supervised and self-supervised learning and finally on broad techniques, such as active, online, and transfer learning.
Machine learning is a large field of study that overlaps with and inherits ideas from many related fields such as artificial intelligence. The focus of the field is learning, that is, acquiring skills or knowledge from experience. Most commonly, this means synthesizing useful concepts from historical data.
As such, there are many different types of learning that we may encounter as a practitioners in the field of machine learning: from whole fields of study to specific techniques. We want to focus on fields of study, such as supervised, unsupervised, and reinforcement learning, hybrid types of learning, such as semi-supervised and self-supervised learning and finally on broad techniques, such as active, online, and transfer learning.
- Learning Problems
- - Supervised Learning
- - Unsupervised Learning
- - Reinforcement Learning
- Hybrid Learning Problems
- - Semi-Supervised Learning
- - Self-Supervised Learning
- - Multi-Instance Learning
- Statistical Inference
- - Inductive Learning
- - Deductive Inference
- - Transductive Learning
- Learning Techniques
- - Multi-Task Learning
- - Active Learning
- - Online Learning
- - Transfer Learning
- - Ensemble Learning
ACIIDS 2022 important dates
Submission of papers: 22 May 2022 (Hard deadline)
Notification of acceptance: 11 July 2022
Camera-ready papers: 25 July 2022
Registration & payment: 25 July 2022
Conference date: 28-30 November 2022
Notification of acceptance: 11 July 2022
Camera-ready papers: 25 July 2022
Registration & payment: 25 July 2022
Conference date: 28-30 November 2022