ECML
2007
Springer
14 years 6 months ago
2007
Springer
Abstract. Robustly estimating the state-transition probabilities of highorder Markov processes is an essential task in many applications such as natural language modeling or protei...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
Boosting is a simple yet powerful modeling technique that is used in many machine learning and data mining related applications. In this paper, we propose a novel scale-space based...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
We extend our recent work on relevant subtask learning, a new variant of multitask learning where the goal is to learn a good classifier for a task-of-interest with too few train...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
ECML
2007
Springer
14 years 6 months ago
2007
Springer
In this paper we investigate the relation between transfer learning in reinforcement learning with function approximation and supervised learning with concept drift. We present a n...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
Decision tree learning algorithms produce accurate models that can be interpreted by domain experts. However, these algorithms are known to be unstable – they can produce drastic...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
The stability of sample based algorithms is a concept commonly used for parameter tuning and validity assessment. In this paper we focus on two well studied algorithms, LSI and PCA...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
We introduce a modified Kalman filter that performs robust, real-time outlier detection, without the need for manual parameter tuning by the user. Systems that rely on high quali...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
Abstract. Constrained clustering investigates how to incorporate domain knowledge in the clustering process. The domain knowledge takes the form of constraints that must hold on th...
ECML
2007
Springer
14 years 6 months ago
2007
Springer
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...