scispace - formally typeset
Open Access

Convex Analysisの二,三の進展について

徹 丸山
- Vol. 70, Iss: 1, pp 97-119
Reads0
Chats0
About
The article was published on 1977-02-01 and is currently open access. It has received 5933 citations till now.

read more

Content maybe subject to copyright    Report

Citations
More filters
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI

Increasing Returns and Long-Run Growth

TL;DR: In this paper, the authors present a fully specified model of long-run growth in which knowledge is assumed to be an input in production that has increasing marginal productivity, which is essentially a competitive equilibrium model with endogenous technological change.
Book

Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers

TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.

Pattern Recognition and Machine Learning

TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI

An Algorithm for Vector Quantizer Design

TL;DR: An efficient and intuitive algorithm is presented for the design of vector quantizers based either on a known probabilistic model or on a long training sequence of data.
References
More filters
Journal ArticleDOI

Nature plays with dice : terrorists do not: Allocating resources to counter strategic versus probabilistic risks

TL;DR: Using linear impact functions, the problems of allocating a limited resource to defend sites that face either probabilistic risk or strategic risk are formulated as optimization problems that are solved explicitly.
Journal ArticleDOI

Adaptive regularization of the NL-means: Application to image and video denoising

TL;DR: A variational approach that corrects the over-smoothing and reduces the residual noise of the NL-means by adaptively regularizing nonlocal methods with the total variation by minimizing an adaptive total variation with a nonlocal data fidelity term is introduced.
Journal ArticleDOI

Preventing bad plans by bounding the impact of cardinality estimation errors

TL;DR: The q-error is defined to measure deviations of size estimates from actual sizes and bounds are provided such that if the q- error is smaller than this bound, the query optimizer constructs an optimal plan.
Posted ContentDOI

Pseudo-potentials and loading surfaces for an endochronic plasticity theory with isotropic damage

TL;DR: In this article, a basic endochronic model with isotropic damage is formulated starting from the postulate of strain equivalence, and the formal tools chosen to formulate the model are those of convex analysis, often used in classical plasticity.
Journal ArticleDOI

Stochastic bounds on sums of dependent risks

TL;DR: In this article, Dhaene and Goovaerts extended these results by showing how to compute bounds on P(S>s) and more generally on E{φ(S)} for monotone, but not necessarily convex functions φ.