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- (1) 'Installing everything'
- This package is self-contained and independent from other external sources. You only need to add the path accordingly:
- Setup path: initWorkspaceGPMulticlass
- (2) GP models
- Get to know GP models on a simple 1D example: multiclass_gp_1D_example
- (3) Incremental learning
- Compare efficient incremental learning and training from scratch: evaluateIncrementalLearning
- What should you expect:
- - figure 1: computation times for model updates while adding several samples are displayed. The blue line (efficient updates)
- should be below the red one (learning from scratch). Results are averaged over several runs
- - figure 2: accuracies obtained while incrementall increasing the training set. Both curves should be identical - if not, something
- went wrong within the process of model updates. Since the toy example only uses artificial data, the accuracy curve is not
- expected to look super fancy
- NOTE:
- The technique for incrementally update GP models updates 'the whole model', although for classification, only alpha actually matters. Therefore, if you are only interested
- in classifying samples irrespective of their predictive variance, you might want to use more sophisticated tricks for updating
- the alpha vector only, which can be found exemplarily in
- Alexander Freytag and Erik Rodner and Paul Bodesheim and Joachim Denzler:
- "Labeling examples that matter: Relevance-Based Active Learning with Gaussian Processes".
- Proceedings of the German Conference on Pattern Recognition (GCPR), 2013.
- If you should be further interested in GP equipped with histogram intersection kernels, you might want to use our GPHIK implementations instead, which avoid
- explicit covariance construction and are algorithmically much more efficient (eg O(n logn) in training compared to O(n^3) ). Refer to the following source for further details
- Erik Rodner and Alexander Freytag and Paul Bodesheim and Joachim Denzler:
- " Large-Scale Gaussian Process Classification with Flexible Adaptive Histogram Kernels".
- Proceedings of the European Conference on Computer Vision (ECCV), 2012.
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