新規登録 | ログイン | FAQ

[?]

CiteULike is a free online bibliography manager. Register and you can start organising your references online.

Recent | Unread | Search | Authors | Tags | Export

How to Compare Different Loss Functions and Their Risks

by: Ingo Steinwart

Constructive Approximation, Vol. 26, No. 2. (August 2007), pp. 225-287.

View FullText article

DOI, IngentaConnect

Reviews [Write a review of this article]

There are no reviews of this article

Find related articles from these CiteULike users

sdvillal, mdreid, Statistical Machine Learning (group), mohdzamrimurah

Find related articles with these CiteULike tags

calibration, classification, class_probability_estimation, density_estimation, error-estimation, loss, loss-functions, no-tag, occ-to-supervised, optimization, surrogate_loss, theory

BibTeX record

RIS record

sdvillal's tags

All tags in sdvillal's library

Filter:

20070622 abduction acii-2007 active-learning agnostic-learning algebra algorithmic-distance-metrics algorithmics algorithm-portfolio anomaly-detection anytime-induction artificial-negatives autoencoders automata bandits bayesian-learning bioinformatics boosting calibration cbir classic-refs clustering coding-theory communication complexity compressed-sensing computer-science computer-vision cost-sensitive co-training data-mining-books data-mining-general datasets dimension-reduction dirichlet discretization distance-metrics distribution-shift dr-for-classification education embedded-fs emotion-models emotion-recognition ensemble-diversity ensembles error-estimation evaluation-methodology extreme-value fda feature-extraction featureless feature-selection fractal free-lunch game-theory general-bayesian-learning generalization geometry gmms graphs high-dimensionality high-performance-computing hmm ica image-processing imbalanced immune-systems incremental-learning inter-feature-predictivity interpretability intrinsic-dimension-estimation kernel-machines lazy-learning learnability learning-bounds learning-on-graphs learning-stability locality loss-functions lpp margin maths mdl mdr mds medical-imaging meta-learning ml-foundations ml-philosophy ml-review ml-software model-selection multiclass multiclass-to-occ multi-instance multilabel multimedia-ir natural-optimization nearest-neighbors negative-example-selection negative-features neural-networks non-stationary novels numerical-computing oca occ-applications occ-comparison occ-dimension-reduction occ-for-multiclass occ-high-dimensional occ-nearest-neighbor occ-neural-networks occ-others occ-parameter-fitting occ-reconstruction occ-statistical occ-support-vector occ-survey occ-to-multiclass occ-to-supervised occ-vs-supervised occ-with-outliers online-learning optimization overfitting partially-labeled-data pca pca-computation phase-transition population-based practical-scenarios presentations programming proportional-data pso publish-or-perish randomness ranking reductions rejection-option reject-option roc sat scalability scale-sensitive scientific-software seminal semi-supervised sets-of-features simplicity software-engineering speaker-verification spectral statistics structured-domains text-classification theoretical-computer-science time-series uniform-convergence unobserved-features unsupervised-dr-for-classification