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Learning Additive Models Online with Fast Evaluating Kernels
[chapter]
2001
Lecture Notes in Computer Science
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We develop three new techniques to build on the recent advances in online learning with kernels. ...
First, we show that an exponential speed-up in prediction time per trial is possible for such algorithms as the Kernel-Adatron, the Kernel-Perceptron, and ROMMA for specific additive models. ...
A portion of this research was undertaken while at the Computer Learning Research Centre at Royal Holloway University. ...
doi:10.1007/3-540-44581-1_29
fatcat:cgkijnnbtfc2fk4zpl75fzcdxq
Dual Memory Architectures for Fast Deep Learning of Stream Data via an Online-Incremental-Transfer Strategy
[article]
2015
arXiv
pre-print
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We aim to train deep models from new data that consists of new classes, distributions, and tasks at minimal computational cost, which we call online deep learning. ...
The proposed architecture consists of deep representation learners and fast learnable shallow kernel networks, both of which synergize to track the information of new data. ...
Online Incremental Learning Algorithms for Fast Memory
Shallow Kernel Networks on the Neural Networks We introduce the fast memory; shallow kernel networks on the neural networks. ...
arXiv:1506.04477v1
fatcat:ryij5slnsjhi7acauu3bhnfkry
A sliding-window online fast variational sparse Bayesian learning algorithm
2011
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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In this work a new online learning algorithm that uses automatic relevance determination (ARD) is proposed for fast adaptive nonlinear filtering. ...
A sequential decision rule for inclusion or deletion of basis functions is obtained by applying a recently proposed fast variational sparse Bayesian learning (SBL) method. ...
This method provides a fast decision rule for selecting model components and allows for addition of new basis functions as well as deletion of components currently in the model. ...
doi:10.1109/icassp.2011.5946747
dblp:conf/icassp/BuchgraberSP11
fatcat:3wzxop3vszejlnvdyrmostp3j4
KeLP: a Kernel-based Learning Platform for Natural Language Processing
2015
Proceedings of ACL-IJCNLP 2015 System Demonstrations
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KeLP is a Java framework that enables fast and easy implementation of kernel functions over discrete data, such as strings, trees or graphs and their combination with standard vectorial kernels. ...
Additionally, it provides several kernel-based algorithms, e.g., online and batch kernel machines for classification, regression and clustering, and a Java environment for easy implementation of new algorithms ...
(ii) Additional-algorithm packages, e.g., online kernel machines, Nyström method (Williams and Seeger, 2001) and label sequence learning (Altun et al., 2003) , and (iii) additional-kernel packages, which ...
doi:10.3115/v1/p15-4004
dblp:conf/acl/FiliceCCB15
fatcat:7tk3wpimbjgunif4h6flggfwlu
Physics-informed Gaussian Process for Online Optimization of Particle Accelerators
[article]
2020
arXiv
pre-print
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The ability to inform the machine-learning model with physics may have wide applications in science. ...
Instead, here we use a fast approximate model from physics simulations to design the GP model. ...
For systems with complex high-dimensional data structures, expressive kernels facilitate efficient learning from online acquired data. ...
arXiv:2009.03566v1
fatcat:tke6mmylqneijgonxz4qteryt4
Online Attentive Kernel-Based Temporal Difference Learning
[article]
2022
arXiv
pre-print
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Experimental evaluations showed that OAKTD outperformed several Online Kernel-based Temporal Difference (OKTD) learning algorithms in addition to the Temporal Difference (TD) learning algorithm with Tile ...
Therefore, a simpler and more adaptive approach is introduced to evaluate value function with the kernel-based model. ...
To accurately evaluate performance of the attentive kernel-based model as sparse representation, we compared our proposed OAKTD 4 with OKTD, OSKTD and TD with Tile Coding. ...
arXiv:2201.09065v1
fatcat:yo66jiysnfheviqmileumklv4a
Dynamic Kernel Distillation for Efficient Pose Estimation in Videos
[article]
2019
arXiv
pre-print
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In particular, DKD introduces a light-weight distillator to online distill pose kernels via leveraging temporal cues from the previous frame in a one-shot feed-forward manner. ...
To address this issue, we propose a novel Dynamic Kernel Distillation (DKD) model to facilitate small networks for estimating human poses in videos, thus significantly lifting the efficiency. ...
It can also fast distill pose kernels in a one-shot manner, avoiding complex iterating utilized by previous online kernel learning models [4, 27] . ...
arXiv:1908.09216v1
fatcat:lqwphxayu5ddtgdb2mebip2gbu
Large-scale Online Kernel Learning with Random Feature Reparameterization
2017
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
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A typical online kernel learning method faces two fundamental issues: the complexity in dealing with a huge number of observed data points (a.k.a the curse of kernelization) and the difficulty in learning ...
This view further inspires a direct application of stochastic gradient descent for updating our model under an online learning setting. ...
This work was partially supported by the Australian Research Council (ARC) and the CoE in Machine Learning and Big Data. ...
doi:10.24963/ijcai.2017/354
dblp:conf/ijcai/NguyenLBP17
fatcat:ls6j4uo4ujajneh7fuym7udi24
Real-Time Local GP Model Learning
[chapter]
2010
Studies in Computational Intelligence
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being sufficiently fast for real-time online learning. ...
Due to reduced computational cost, local Gaussian processes (LGP) can be applied for larger sample-sizes and online learning. ...
It also interesting to investigate further applications of the LGP in humanoid robotics with 35 of more DoFs and learning other types of the control such as operational space control. ...
doi:10.1007/978-3-642-05181-4_9
fatcat:liqxstsdxbftzbqupzcyoanw2q
Diversity-Promoting Deep Reinforcement Learning for Interactive Recommendation
[article]
2019
arXiv
pre-print
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We performed extensive offline experiments as well as simulated online experiments with real world datasets to demonstrate the effectiveness of the proposed model. ...
through an actor-critic reinforcement learning framework. ...
For online evaluation, we build a simulator to compare D 2 RL with C 2 UCB. ...
arXiv:1903.07826v1
fatcat:s5nlfafmvjhmlct5gar2qkcrc4
Using model knowledge for learning inverse dynamics
2010
2010 IEEE International Conference on Robotics and Automation
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The results show that the semiparametric models learned with rigid body dynamics as prior outperform the standard rigid body dynamics models on real data while generalizing better for unknown parts of ...
We present two possible semiparametric regression approaches, where the knowledge of the physical model can either become part of the mean function or of the kernel in a nonparametric Gaussian process ...
of the additional kernel k(·, ·) to the learning process. ...
doi:10.1109/robot.2010.5509858
dblp:conf/icra/Nguyen-TuongP10
fatcat:uftusyvf5vboxlrydvuhxehevu
Incremental online sparsification for model learning in real-time robot control
2011
Neurocomputing
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In this paper, we propose a framework for online, incremental sparsification with a fixed budget designed for fast real-time model learning. ...
In combination with an incremental learning approach such as incremental Gaussian process regression, we obtain a model approximation method which is applicable in real-time online learning. ...
Acknowledgments We would like to thank Bernhard Schölkopf from Max Planck Institute for Biological Cybernetics for helpful discussions and for providing us with relevant machine learning literature of ...
doi:10.1016/j.neucom.2010.06.033
fatcat:2d5ofwppgfccnmsx5mnenezbtq
Robust and Real-Time Visual Tracking Based on Complementary Learners
[chapter]
2018
Lecture Notes in Computer Science
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In addition, we adopt the average peak-to-correlation energy to determine whether to activate and update an online CUR filter for re-detecting the target. ...
In this paper, we exploit the low dimensional complementary features and an adaptive online detector with the average peak-to-correlation energy to improve tracking accuracy and time efficiency. ...
Although several methods fuse multiple features or models to learn the target appearance model, the online models tend to drift due to fast motion and occlusion. ...
doi:10.1007/978-3-319-73600-6_19
fatcat:3jc54mtnsjdrjhxj4lw4oexss4
Long-term correlation tracking
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In addition, we train an online random fern classifier to re-detect objects in case of tracking failure. ...
We show that the correlation between temporal context considerably improves the accuracy and reliability for translation estimation, and it is effective to learn discriminative correlation filters from ...
prevalent in online tracking with an online binary classifier. ...
doi:10.1109/cvpr.2015.7299177
dblp:conf/cvpr/MaYZY15
fatcat:45hg4wqx75esrnolt75um7yjs4
A Neural Network Approach for Online Nonlinear Neyman-Pearson Classification
[article]
2020
arXiv
pre-print
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As a result, we obtain an expedited online adaptation and powerful nonlinear Neyman-Pearson modeling. ...
We sequentially learn the SLFN with stochastic gradient descent updates based on a Lagrangian NP objective. ...
By enforcing a strict hard constraint on FPR with a very large κ ∞, one can immediately reject models (while evaluating various models) that violate FPR constraint with even a slight positive deviation ...
arXiv:2006.08001v2
fatcat:wk6hjb5uirfo5ecijzneib2ede
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