Impact of Regularization on the Model Space for Time Series Classification

Aswolinskiy W, Reinhart F, Steil JJ. Impact of Regularization on the Model Space for Time Series Classification. In: New Challenges in Neural Computation (NC2). 2015: 49-56

Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks

Aswolinskiy W, Steil JJ. Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks. In: Proceedings of the Workshop on New Challenges in Neural Computation. Machine Learning Reports. 2016

Goal Babbling with direction sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot

Rayyes R, Steil JJ. Goal Babbling with direction sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot. In: Proceedings of the workshop on New Challenges in Neural Computation. Machine Learning Reports. Vol 4. 2016: 56-63

Learning in networks of similarity processing neurons

Similarity functions are a very flexible container under which to express knowledge about a problem as well as to capture the meaningful relations in input space. In this paper we describe ongoing research using similarity functions to find more convenient representations for a problem –a crucial factor for successful learning– such that subsequent processing can be delivered to linear or non-linear modeling methods. The idea is tested in a set of challenging problems, characterized by a mixture of data types and different amounts of missing values. We report a series of experiments testing the idea against two more traditional approaches, one ignoring the knowledge about the dataset and another using this knowledge to pre-process it. The preliminary results demonstrate competitive or better generalization performance than that found in the literature. In addition, there is a considerable enhancement in the interpretability of the obtained models.Postprint (published version

Incremental learning of action models as HMMs over qualitative trajectory representations

Panzner M, Cimiano P. Incremental learning of action models as HMMs over qualitative trajectory representations. Presented at the Workshop on New Challenges in Neural Computation (NC2), Aachen.In this paper we present an incremental approach to learning generative models of object manipulation actions as HMMs over qualitative relations between two objects. We compare the incremental approach against a traditional batch training baseline and show that the resulting qualitative action models are capable of one-shot learning after just one seen example while displaying good generalization behavior as more data becomes available