Linear Supervised Transfer Learning Toolbox

Paaßen B, Schulz A (2017)
Bielefeld University.

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Abstract / Bemerkung
This Matlab (R) toolbox provides several algorithms to learn a linear mapping from an n-dimensional source space to an m-dimensional target space, such that it makes a classification or clustering model that has been trained in the source space applicable in the target space. The source space model is assumed to be either a vector quantization model (such as learning vector quantizations and variations thereof, neural gas or k-Means) or a (labelled) mixture of Gaussians. The target space may be any vector space, but this toolbox will typically fail if the relationship between source and target space is highly nonlinear. In contrast, this toolbox is particularly effective if the difference between source and target space can be expressed in terms of simple, linear transformations such as rotations and scalings.
Stichworte
transfer learning; expectation maximization; Gaussian mixture models; learning vector quantization
Erscheinungsjahr
2017
Page URI
https://pub.uni-bielefeld.de/record/2912671

Zitieren

Paaßen B, Schulz A. Linear Supervised Transfer Learning Toolbox. Bielefeld University; 2017.
Paaßen, B., & Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University. doi:10.4119/unibi/2912671
Paaßen, B., and Schulz, A. (2017). Linear Supervised Transfer Learning Toolbox. Bielefeld University.
Paaßen, B., & Schulz, A., 2017. Linear Supervised Transfer Learning Toolbox, Bielefeld University.
B. Paaßen and A. Schulz, Linear Supervised Transfer Learning Toolbox, Bielefeld University, 2017.
Paaßen, B., Schulz, A.: Linear Supervised Transfer Learning Toolbox. Bielefeld University (2017).
Paaßen, Benjamin, and Schulz, Alexander. Linear Supervised Transfer Learning Toolbox. Bielefeld University, 2017.
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Material in PUB:
Wird zitiert von
Expectation maximization transfer learning and its application for bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2018)
Neurocomputing 298: 122-133.
Wird zitiert von
An EM transfer learning algorithm with applications in bionic hand prostheses
Paaßen B, Schulz A, Hahne J, Hammer B (2017)
In: Proceedings of the 25th European Symposium on Artificial Neural Networks (ESANN 2017). Verleysen M (Ed); Bruges: i6doc.com: 129-134.

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