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ORCIDMachine Learning, Volume 91
Volume 91, Number 1, April 2013
- Lena Chekina, Dan Gutfreund, Aryeh Kontorovich, Lior Rokach, Bracha Shapira
:
Exploiting label dependencies for improved sample complexity. 1-42 - Alain Rakotomamonjy, Rémi Flamary, Florian Yger:
Learning with infinitely many features. 43-66 - Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis:
Ranking data with ordinal labels: optimality and pairwise aggregation. 67-104 - Marco Grzegorczyk, Dirk Husmeier:
Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models. 105-154
Volume 91, Number 2, May 2013
- Koby Crammer, Alex Kulesza, Mark Dredze:
Adaptive regularization of weight vectors. 155-187 - Masanori Kawakita, Takafumi Kanamori:
Semi-supervised learning with density-ratio estimation. 189-209 - Elmar Diederichs, Anatoli B. Juditsky, Arkadi Nemirovski, Vladimir G. Spokoiny:
Sparse non Gaussian component analysis by semidefinite programming. 211-238 - Katsumi Inoue, Andrei Doncescu, Hidetomo Nabeshima:
Completing causal networks by meta-level abduction. 239-277
Volume 91, Number 3, June 2013
- Yi-Hao Kao, Benjamin Van Roy:
Learning a factor model via regularized PCA. 279-303 - Arash Afkanpour, Csaba Szepesvári, Michael Bowling:
Alignment based kernel learning with a continuous set of base kernels. 305-324 - Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert J. Kappen:
Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. 325-349
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