Add to ORKGIn Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples derived from the training set. When the model size grows with the complexity of the task, such approaches are so computationally demanding that the adoption of comprehensive models is not always viable. In this paper, a general framework aimed at minimizing this problem is proposed: multiple classifiers are stratified and dynamically invoked according to increasing levels of complexity corresponding to incrementally more expressive representation spaces. Computationally expensive inferences are thus adopted only when the classification at lower levels is too uncertain over an individual instance. The application of complex functions is thus avoid...
DoctorIn the last decade, the kernel methods have contributed to significantadvances in research are...
National audienceThe Support Vector Machine (SVM) is an acknowledged powerful tool for building clas...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
In Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples deri...
In Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples deri...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
In many Natural Language Processing tasks, kernel learning allows to define robust and effective sys...
Kernel methods are popular and effective techniques for learning on structured data, such as trees a...
Very high dimensional learning systems become theoretically possible when training examples are abun...
Kernel-based methods for NLP tasks have been shown to enable robust and effective learning, although...
Kernel learning algorithms are currently becoming a standard tool in the area of machine learning an...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
In online learning with kernels, it is vital to control the size (budget) of the support set because...
We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a pa...
In the 1990s, a new type of learning algorithm was developed, based on results from statistical lear...
DoctorIn the last decade, the kernel methods have contributed to significantadvances in research are...
National audienceThe Support Vector Machine (SVM) is an acknowledged powerful tool for building clas...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...
In Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples deri...
In Kernel-based Learning the targeted phenomenon is summarized by a set of explanatory examples deri...
Kernel Learning is widely used in pattern recognition and classification problems. We look at the be...
In many Natural Language Processing tasks, kernel learning allows to define robust and effective sys...
Kernel methods are popular and effective techniques for learning on structured data, such as trees a...
Very high dimensional learning systems become theoretically possible when training examples are abun...
Kernel-based methods for NLP tasks have been shown to enable robust and effective learning, although...
Kernel learning algorithms are currently becoming a standard tool in the area of machine learning an...
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, stati...
In online learning with kernels, it is vital to control the size (budget) of the support set because...
We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a pa...
In the 1990s, a new type of learning algorithm was developed, based on results from statistical lear...
DoctorIn the last decade, the kernel methods have contributed to significantadvances in research are...
National audienceThe Support Vector Machine (SVM) is an acknowledged powerful tool for building clas...
Kernel methods are a class of non-parametric learning techniques relying on kernels. A kernel genera...