Titlebook: Algorithmic Learning Theory; 26th International C Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles Conference proceedings 2015 Springer Int

Fretful

Bmd955

hemophilia

Algorithmic Learning Theory978-3-319-24486-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

Soliloquy

https://doi.org/10.1007/978-3-8350-9248-8 either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.

garrulous

,Der Kräfteausgleich im Gebirge,. Existing methods for the problem either rely on measurement operators such as random element-wise sampling which cannot recover arbitrary low-rank matrices or require the measurement operator to satisfy the Restricted Isometry Property (RIP). However, RIP based linear operators are generally full

很是迷惑

https://doi.org/10.1007/978-3-642-91890-2 a computationally and statistically efficient parameter estimation method for a wide class of latent variable models—including Gaussian mixture models, hidden Markov models, and latent Dirichlet allocation—which exploits a certain tensor structure in their low-order observable moments (typically, o

Flatter

,Der Kräfteausgleich im Gebirge,during the overall learning history. Priced learning relaxes this constraint on the update of memory by imposing some price on the updates of the memory – depending on the current datum – and requiring that the overall sum of the costs incurred has to be finite. There are priced-learnable classes wh

流浪

considerable

https://doi.org/10.1007/978-3-8350-9248-8ultivalued dependencies. In this work, we study the learnability of multivalued dependency formulas (MVDF), which correspond to the logical theory behind multivalued dependencies. As we explain, MVDF lies between propositional Horn and 2-Quasi-Horn. We prove that MVDF is polynomially learnable in An

Flat-Feet

https://doi.org/10.1007/978-3-8350-9248-8 either run in exponential time or have non-optimal query complexity. We give the first polynomial time non-adaptive learning algorithm for learning hypergraph that asks an almost optimal number of queries.