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Related papers: Some new maximum VC classes

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Let G be a finite group of order n and V an irreducible representation over the complex numbers of dimension d. For some nonnegative number e, we have n=d(d+e). If e is small, then the character of V has unusually large degree. We fix e and…

Group Theory · Mathematics 2008-08-28 Noah Snyder

We construct a new kind of measures, called projection families, which generalize the classical notion of vector and operator-valued measures. The maximal class of reasonable functions admits an integral with respect to a projection family,…

Functional Analysis · Mathematics 2025-10-15 Luis A. Cedeño-Pérez , Hernando Quevedo

Support Vector Machines (SVMs) are one of the most popular supervised learning models to classify using a hyperplane in an Euclidean space. Similar to SVMs, tropical SVMs classify data points using a tropical hyperplane under the tropical…

Machine Learning · Computer Science 2022-10-05 Ruriko Yoshida , Misaki Takamori , Hideyuki Matsumoto , Keiji Miura

In the signal processing and statistics literature, the minimum description length (MDL) principle is a popular tool for choosing model complexity. Successful examples include signal denoising and variable selection in linear regression,…

Signal Processing · Electrical Eng. & Systems 2022-01-28 Zhenyu Wei , Raymond K. W. Wong , Thomas C. M. Lee

Classes of multivariate and cone valued infinitely divisible Gamma distributions are introduced. Particular emphasis is put on the cone-valued case, due to the relevance of infinitely divisible distributions on the positive semi-definite…

Probability · Mathematics 2015-03-19 Victor Pérez-Abreu , Robert Stelzer

We establish a maximum principle for a two-point function in order to analyze the convexity of level sets of harmonic functions. We show that this can be used to prove a strict convexity result involving the smallest principal curvature of…

Analysis of PDEs · Mathematics 2018-04-25 Ben Weinkove

The VC-dimension of a set system is a way to capture its complexity and has been a key parameter studied extensively in machine learning and geometry communities. In this paper, we resolve two longstanding open problems on bounding the…

Machine Learning · Computer Science 2018-07-23 Monika Csikos , Andrey Kupavskii , Nabil H. Mustafa

Finite frames can be viewed as mass points distributed in $N$-dimensional Euclidean space. As such they form a subclass of a larger and rich class of probability measures that we call probabilistic frames. We derive the basic properties of…

Probability · Mathematics 2017-09-04 Martin Ehler , Kasso A. Okoudjou

We study the representation growth of alternating and symmetric groups in positive characteristic and restricted representation growth for the finite groups of Lie type. We show that the the number of representations of dimension at most n…

Representation Theory · Mathematics 2019-12-19 Robert Guralnick , Michael Larsen , Pham Huu Tiep

We introduce a countable collection of positivity classes for Hermitian symmetric functions on a complex manifold, and establish their basic properties. We study a related notion of stability. The first main result shows that, if the…

Complex Variables · Mathematics 2007-05-23 John P. D'Angelo , Dror Varolin

Fix positive integers $k$ and $d$. We show that, as $n\to\infty$, any set system $\mathcal{A} \subset 2^{[n]}$ for which the VC dimension of $\{ \triangle_{i=1}^k S_i \mid S_i \in \mathcal{A}\}$ is at most $d$ has size at most…

Combinatorics · Mathematics 2018-10-16 Stijn Cambie , António Girão , Ross J. Kang

For an open set $V\subset\mathbb{C}^n$, denote by $\mathscr{M}_{\alpha}(V)$ the family of $\alpha$-analytic functions that obey a boundary maximum modulus principle. We prove that, on a bounded domain $\Omega\subset \mathbb{C}^n$, with…

Complex Variables · Mathematics 2018-09-05 Abtin Daghighi , Frank Wikström

For a complex manifold equipped with an anti-holomorphic involution, which is referred to as a real variety, the Smith-Thom inequality states that the total $\mathbb{F}_2$-Betti number of the real locus is not greater than the total…

Algebraic Geometry · Mathematics 2025-05-07 Lie Fu

A wide variety of machine learning algorithms such as support vector machine (SVM), minimax probability machine (MPM), and Fisher discriminant analysis (FDA), exist for binary classification. The purpose of this paper is to provide a…

Machine Learning · Computer Science 2012-06-22 Akiko Takeda , Hiroyuki Mitsugi , Takafumi Kanamori

The support vector machine (SVM) and minimum Euclidean norm least squares regression are two fundamentally different approaches to fitting linear models, but they have recently been connected in models for very high-dimensional data through…

Machine Learning · Computer Science 2021-10-28 Navid Ardeshir , Clayton Sanford , Daniel Hsu

A characterization of a class of optimal three-weight cyclic codes of dimension 3 over any finite field was recently presented in [10]. Shortly after this, a generalization for the sufficient numerical conditions of such characterization…

Information Theory · Computer Science 2016-09-26 Gerardo Vega

This paper considers mathematical programs, whose constraints are expressed by a parameterized vector equilibrium problem. The latter is a well recognized framework, which is able to cover multicriteria optimization, vector variational…

Optimization and Control · Mathematics 2022-10-18 Amos Uderzo

In this study, we establish a significant connection between certain subclasses of complex order univalent functions and the Mittag-Leffler-type Poisson distribution series. We provide criteria for these series to belong to the specific…

General Mathematics · Mathematics 2024-08-06 K. Marimuthu , A. Jeeva , Nasir Ali

We give an upper bound in O(d ^((n+1)/2)) for the number of critical points of a normal random polynomial with degree d and at most n variables. Using the large deviation principle for the spectral value of large random matrices we obtain…

Numerical Analysis · Mathematics 2010-07-12 Jean-Pierre Dedieu , Gregorio Malajovich

We investigate the Probably Approximately Correct (PAC) property of scenario decision algorithms, which refers to their ability to produce decisions with an arbitrarily low risk of violating unknown safety constraints, provided a sufficient…

Machine Learning · Computer Science 2025-08-28 Guillaume O. Berger , Raphaël M. Jungers