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Algorithms for Hyper-Parameter Optimization
TLDR
This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.
Learning and Design of Principal Curves
TLDR
This work defines principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution, making it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction.
Collaborative hyperparameter tuning
TLDR
A generic method to incorporate knowledge from previous experiments when simultaneously tuning a learning algorithm on new problems at hand is proposed and is demonstrated in two experiments where it outperforms standard tuning techniques and single-problem surrogate-based optimization.
Aggregate features and ADABOOST for music classification
TLDR
An algorithm that predicts musical genre and artist from an audio waveform using the ensemble learner ADABOOST and evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features.
Correlation of the highest-energy cosmic rays with nearby extragalactic objects.
Using data collected at the Pierre Auger Observatory during the past 3.7 years, we demonstrated a correlation between the arrival directions of cosmic rays with energy above 6 x 10(19) electron volts
Observation of the suppression of the flux of cosmic rays above 4 x 10 (19) eV.
TLDR
The energy spectrum of cosmic rays above 2.5 x 10;{18} eV, derived from 20,000 events recorded at the Pierre Auger Observatory, is described and the hypothesis of a single power law is rejected with a significance greater than 6 standard deviations.
Principal curves: learning, design, and applications
TLDR
The main result here is the first known consistency proof of a principal curve estimation scheme, and an application of the polygonal line algorithm to hand-written character skeletonization.
Piecewise Linear Skeletonization Using Principal Curves
TLDR
The results indicated that the proposed algorithm can find a smooth medial axis in the great majority of a wide variety of character templates and that it substantially improves the pixel-wise skeleton obtained by traditional thinning methods.
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