Search Results (2)
Scalable neuro-symbolic machine learning
With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …
Algorithms for Learning Regression Trees and Ensembles on Evolving Data Streams
In this thesis we address the problem of learning various types of decision trees from timechanging data streams. In particular, we study online machine learning algorithms for learning regression trees, linear model trees, option trees for regression, multi-target model trees, and ensembles of model trees from data streams. These are …