000			02044nas a22003015i 4500
001			3059-3492
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022			_a3059-3492
024	7		_a44439 _2local
210	1	0	_aMath. Found. Mach. Learn.
245	1	0	_aMathematical Foundations of Machine Learning _h[electronic resource] / _cedited by Helmut Bölcskei.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer.
300			_bonline resource.
520			_aMathematical Foundations of Machine Learning (MFML) is a forum for the publication of papers concerned with all pure and applied mathematical aspects of machine learning, with a particular focus on foundational work. Areas of interest include, inter alia, functional analysis, approximation theory, harmonic analysis, partial differential equations, combinatorics, probability theory, statistics, geometry, algebra, analytic number theory, complex analysis, linear algebra, group theory, representation theory, theoretical computer science, complexity theory, numerical analysis, algebraic geometry, mathematical logic, geometric measure theory, and optimization theory. All papers must be characterized by originality and mathematical rigour. We explicitly encourage papers of conceptual value residing in the synthesis of mathematical theories and their application to fundamental problems in machine learning theory and practice. Survey papers are welcome as well and must exhibit originality in the synthesis of the material and their vantage point. .
650		0	_aMathematics.
650		0	_aMathematics.
650		0	_aMachine learning.
650	1	4	_aMathematics.
650	2	4	_aMathematics and Computing.
650	2	4	_aMachine Learning.
700	1		_aBölcskei, Helmut. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt
710	2		_aSpringerLink (Online service)
856	4	0	_uhttp://link.springer.com/journal/44439 _zHybrid
999			_c3548 _d3548