Results on a NIST language recognition task show that the SDTMKL method is quite effective and can further improve system performance when combined with NAP.
Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing ...
Distribution mismatch between training and test data can greatly deteriorate the performance of language recognition. Some effective methods for ...
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What is multiple kernel learning technique?
What is domain in transfer learning?
Multiple kernel learning (MKL) is a machine learning approach that allows for the integration of multiple features, such as genes, proteins, and metabolites,
Missing: transfer language
In this paper, we generalize the framework of MKL for cross-domain feature learning and propose a novel. Transfer Feature Representation (TFR) algorithm. TFR.
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Sep 9, 2023 · In this work, we present a general, scalable framework for performing transfer learning with kernel methods. Unlike prior work, our framework ...
May 26, 2016 · Abstract In this paper, we invest the domain transfer learning problem with multi-instance data. We assume we already have a well-trained ...
May 28, 2016 · Multiple kernel learning allows for an optimal kernel function to be learned in a computationally efficient manner. The paper by Duan [27] ...
Sep 22, 2013 · Domain Transfer Multiple Kernel Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(3):465-479, March 2012 ...
In this paper, a kernel choice method is proposed for domain adaption, referred to as Optimal Kernel Choice Domain Adaption (OKCDA).
Run Deep Learning Frameworks Including Apache MXNet, TensorFlow, Caffe, Theano and Torch. Supports Several AI Use Cases Including Computer Vision and Natural Language Processing. AI Products.