Add to ORKGOne of the main assumptions in machine learning is that sufficient training data is avail-able in advance and batch learning can be applied. However, because of the dynamics in a lot of applications, this assumption will break down in almost all cases over time. There-fore, classifiers have to be able to adapt themselves when new training data from existing or new classes becomes available, training data is changed or should be even removed. In this paper, we present a method allowing efficient incremental learning of a Gaussian process classifier. Experimental results show the benefits in terms of needed computation times compared to building the classifier from the scratch
Gaussian process (GP) models are powerful tools for Bayesian classification, but their limitation is...
The final publication is available at link.springer.comProgramming by demonstration techniques facil...
We present a new method for the incremental train-ing of multiclass Support Vector Machines that pro...
Gaussian processes modeling is a relatively new modeling method which is due to its good features mo...
Variational methods have been recently considered for scaling the training process of Gaussian proce...
Many real world problems involve the challenging context of data streams, where classifiers must be ...
We present a novel multi-output Gaussian process model for multi-class classification. We build on t...
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stoc...
Gaussian Processes are powerful tools in machine learning which offer wide applicabil-ity in regress...
Many real classification tasks are oriented to sequence (neighbor) la-beling, that is, assigning a l...
We develop a simulation-based method for the online updating of Gaussian process regression and clas...
Transfer Learning is an emerging framework for learning from data that aims at intelligently transf...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
One pass learning updates a model with only a single scan of the dataset, without storing historical...
Gaussian process (GP) models are powerful tools for Bayesian classification, but their limitation is...
The final publication is available at link.springer.comProgramming by demonstration techniques facil...
We present a new method for the incremental train-ing of multiclass Support Vector Machines that pro...
Gaussian processes modeling is a relatively new modeling method which is due to its good features mo...
Variational methods have been recently considered for scaling the training process of Gaussian proce...
Many real world problems involve the challenging context of data streams, where classifiers must be ...
We present a novel multi-output Gaussian process model for multi-class classification. We build on t...
This work introduces the Efficient Transformed Gaussian Process (ETGP), a new way of creating C stoc...
Gaussian Processes are powerful tools in machine learning which offer wide applicabil-ity in regress...
Many real classification tasks are oriented to sequence (neighbor) la-beling, that is, assigning a l...
We develop a simulation-based method for the online updating of Gaussian process regression and clas...
Transfer Learning is an emerging framework for learning from data that aims at intelligently transf...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, p...
One pass learning updates a model with only a single scan of the dataset, without storing historical...
Gaussian process (GP) models are powerful tools for Bayesian classification, but their limitation is...
The final publication is available at link.springer.comProgramming by demonstration techniques facil...
We present a new method for the incremental train-ing of multiclass Support Vector Machines that pro...