Fast kernel classifiers with online and active learning

Date

2005-09-01

Author

Bordes, A
Ertekin Bolelli, Şeyda
Weston, J
Bottou, L

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

55
views

0
downloads

Very high dimensional learning systems become theoretically possible when training examples are abundant. The computing cost then becomes the limiting factor. Any efficient learning algorithm should at least take a brief look at each example. But should all examples be given equal attention?

Subject Keywords

Convergence

URI

https://hdl.handle.net/11511/54343

Journal

JOURNAL OF MACHINE LEARNING RESEARCH

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Fast and accurate solutions of scattering problems involving dielectric objects with moderate and low contrasts Ergül, Özgür Salih (2007-08-31) We consider the solution of electromagnetic scattering problems involving relatively large dielectric objects with moderate and low contrasts. Three-dimensional objects are discretized with Rao-Wilton-Glisson functions and the scattering problems are formulated with surface integral equations. The resulting dense matrix equations are solved iteratively by employing the multilevel fast multipole algorithm. We compare the accuracy and efficiency of the results obtained by employing various integral equations ...
The neural network technique - 1: a general exposition Tulunay, Yurdanur; Tulunay, E; Senalp, ET (Elsevier BV, 2004-01-01) Near earth space processes are highly complex and nonlinear and mathematical modeling based on first physical principals is usually difficult or impossible. For such cases data driven modeling methods are recommended to be used in parallel with mathematical modeling approach. Highly non-linear processes in the near-earth space are advantageously dealt with using data-driven modeling techniques in the neural network (NN) approach. The only basic requirement for its application is the availability of represen...
Deep learning algorithm applied to daily solar irradiation estimations Akbaba, Erol C.; Yüce, Emre; Akinoglu, Bulent G. (2018-07-02) Deep learning is applied in many research areas, and in many of them remarkable outcomes are attained compared to conventional methods. There are quite a number of studies also in the estimation of solar irradiation. Estimation of solar irradiation is vitally important for the design of solar energy systems. In this work, multi-layer perceptron (MLPs) method of deep learning is used to develop an estimation method for calculating horizontal daily solar irradiation and the results are compared with classical...
Compressed Representation of High Dimensional Channels using Deep Generative Networks Doshi, Akash; Balevi, Eren; Andrews, Jeffrey G. (2020-05-01) © 2020 IEEE.This paper proposes a novel compressed representation for high dimensional channel matrices obtained by optimization of the input to a deep generative network. Channel estimation using generative networks constrains the reconstructed channel to lie in the range of the generative model, which allows it to outperform conventional channel estimation techniques in the presence of limited number of pilots. It also eliminates the need for explicit knowledge of the sparsifying basis for mmWave multiple...
Efficient simulation-based discrete optimization Guikema, SD; Davidson, RA; Ertuğrul, Zehra (2004-01-01) In many practical applications of simulation it is desirable to optimize the levels of integer or binary variables that are inputs for the simulation model. In these cases, the objective function must often be estimated through an expensive simulation process, and the optimization problem is NP-hard, leading to a computationally difficult problem. We investigate efficient solution methods for this problem, and we propose an approach that reduces the number of runs of the simulation by using ridge regression...

Citation Formats

A. Bordes, Ş. Ertekin Bolelli, J. Weston, and L. Bottou, “Fast kernel classifiers with online and active learning,” JOURNAL OF MACHINE LEARNING RESEARCH, pp. 1579–1619, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54343.