Searching for "Algorithmic Luckiness." – sorted by Relevance.
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Algorithmic luckiness
- by the luckiness framework of Shawe-Taylor et al. (1998), we study learning algorithms more directly and in a way
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Random subclass bounds
- obtained in the compression scheme [7], luckiness [13], and algorithmic luckiness [6] frameworks in which
- Cited by 5 (1 self) – Add To MetaCart
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On the Importance of Small Coordinate Projections
- , the luckiness [14] and the algorithmic luckiness [5], can be used to establish generalization bounds
- Cited by 1 (0 self) – Add To MetaCart
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Learning the Kernel with Hyperkernels
- (?, Theorem 9) and the Algorithmic Luckiness framework (Herbrich and Williamson, 2002, Theorem 17). 2
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Learning and Generalization: Theoretical Bounds
- learning algorithms. The main ideas are very similar to the luckiness framework. One of the motiva11 tions
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The Bootstrap Widrow-Hoff Rule as a Cluster-Formation Algorithm
- process should have been. In the "bootstrapping" or "decisiOn-directed" algorithm (Lucky 1966), the actual
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Combinatorial Feature Selection Problems
- K consists of all the dimensions picked in this way. Call a run of the algorithm lucky when
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Scalable parallel algorithms for fpt problems
- by parallel algorithms. As we shall see, parallel algorithms may be very lucky, or very unlucky
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Precise Fixpoint Computation Through StrategyIteration
- will not result in terminating algorithms. For the lucky case where all numbers are non-negative, polynomial
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A Time-Space Tradeoff for Boolean Matrix Multiplication
- of A. If the algorithm is lucky, the entire row is filled with zeros. In that case, the algorithm
- Cited by 5 (0 self) – Add To MetaCart

