• Aucun résultat trouvé

How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets

N/A
N/A
Protected

Academic year: 2021

Partager "How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets"

Copied!
25
0
0

Texte intégral

Loading

Figure

Table 1: Handwritten digit recognition error rates (%)
Table 2: Object recognition error (%)
Table 3: Best Token Error Rates on Test Set (%) Model Bengali Cantonese
Table 4: Combining best performing kernel and DNN models Dataset MNIST-6.7 CIFAR-10 Bengali Cantonese
+7

Références

Documents relatifs

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

• Our seond KPCA based transfer learning approah (KPCA-TL-LT) further aligns the resulting mathed KPCA representations of soure and target by a linear transformation.. The result

Conversely, it is easy to check that the latter condition implies that T a is bounded.. Moreover Λ is composed of eigenvalues λ associated with finite dimensional vector

In the last years, many works were devoted to the estimates, or asymptotics, of the correlation of two local observables, (or Ursell functions of n local observables), for

These kernels are either based on a combination of graphedit distances (trivial kernel, zeros graphkernel), use the convolution framework introduced by Haussler [11]

The idea here is to retrieve learning algorithm by using the exponential family model with clas- sical statistical principle such as the maximum penalized likelihood estimator or

The ex- planation of a rejected decision in the Argument Classification of a Semantic Role Labeling task (Vanzo et al., 2016), described by the triple e 1 = h’vai in camera da letto’,

Drawing upon kernel formalism, we introduce a strengthened continuous-time convex optimization problem which can be tackled exactly with finite dimensional solvers, and which