Learn about sample complexity in machine learning and how to assess the efficiency of a learning algorithm to determine the data needed for a specific learning goal.
This metric is the measure of complexity for hypothesis space in the average. Similarly in the worst case, we obtain the minimum metric. We make clear the relationship between these measures and the Vapnik-Chervonenkis (VC) dimension. Finally, we show the upper bound on sample complexity ...
Rosasco, "On the sample complexity of subspace learning," in Conference on Neural Information Processing Systems (NIPS), 2013.Alessandro Rudi, Guillermo D Canas, and Lorenzo Rosasco. On the Sample Complexity of Subspace Learning. In Advances in Neural Information Processing Systems, pages 2067-...
Within the framework of pac-learning, we explore the learnability of concepts from samples using the paradigm of sample compression schemes. A sample compr
(1989), adapted ffom Vapnik and Chervonenkis (1971), gives an upper bound on the sample complexity of learning algorithms in terms of the VC dimension of the class. THEOREM 2 (Blumer et al., 1989) Let C be a well-behaved ~ concept class. If the VC dimension of C is d < oc, ...
On the Sample Complexity of Subspace Learning A large number of algorithms in machine learning, from principal component analysis (PCA), and its non-linear (kernel) extensions, to more recent spectral ... A Rudi,GD Canas,L Rosasco - Advances in Neural Information Processing Systems 被引量: ...
In this paper, we ask the following question: given t and δ, what is the minimum n (number of copies of ρ) necessary to implement the unitary e −iρt on an unknown state σ to trace distance at most δ? We call this the sample complexity of Hamiltonian simulation. While the LMR...
Embracing such complexity requires a nuanced approach. Unlike unregularized approaches (e.g., linear regression), supervised machine learning methods allow the opportunity to aggregate disparate small variable effects to inform clinical outcomes while also accounting for complex, interactive, or non-linear...
This lack of closure allows multiplicative recursions (\(c_1\gg 1\)) to include numbers in different residue classes modulo \(q\), and, at the same time, introduce more complexity and non-linearity compared to addition. This observation is often employed by random number generators18 as ...
Learning Factor Graphs in Polynomial Time & Sample Complexity We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor gr... P Abbeel,D Koller,AY Ng,... - 《Journal of Machine Learning Research》 ...