Activities per year
Abstract
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples. Here interactive machine learning (iML) may be of help, having its roots in reinforcement learning, preference learning, and active learning. The term iML is not yet well used, so we define it as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.” This “human-in-the-loop” can be beneficial in solving computationally hard problems, e.g., subspace clustering, protein folding, or k-anonymization of health data, where human expertise can help to reduce an exponential search space through heuristic selection of samples. Therefore, what would otherwise be an NP-hard problem, reduces greatly in complexity through the input and the assistance of a human agent involved in the learning phase.
Translated title of the contribution | Interactives Maschinelles Lernen für die Medizinische Informatik: Wann brauchen wir den Human-in-the-Loop? |
---|---|
Original language | English |
Pages (from-to) | 119-131 |
Number of pages | 12 |
Journal | Brain Informatics |
Volume | 3 |
Issue number | 2 |
Early online date | 31 Mar 2016 |
DOIs | |
Publication status | Published - 17 May 2016 |
Keywords
- Machine Learning
- Health Informatics
ASJC Scopus subject areas
- Artificial Intelligence
Fields of Expertise
- Information, Communication & Computing
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
Fingerprint
Dive into the research topics of 'Interactive Machine Learning for Health Informatics: When do we need the human-in-the-loop?'. Together they form a unique fingerprint.-
20th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems
Andreas Holzinger (Speaker)
6 Sept 2016Activity: Talk or presentation › Invited talk › Science to science
-
Workshop Machine Learning for Biomedicine at TU Graz
Andreas Holzinger (Speaker)
26 Jan 2016Activity: Talk or presentation › Talk at workshop, seminar or course › Science to science