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A Tutorial Introduction

Bernhard Schölkopf, +1 more
- pp 1-22
TLDR
This chapter contains sections titled: Data Representation and Similarity, A Simple Pattern Recognition Algorithm, Some Insights From Statistical Learning Theory, Hyperplane Classifiers, support Vector Classification, Support Vector Regression, Kernel Principal Component Analysis, Empirical Results and Implementations.
Abstract
This chapter contains sections titled: Data Representation and Similarity, A Simple Pattern Recognition Algorithm, Some Insights From Statistical Learning Theory, Hyperplane Classifiers, Support Vector Classification, Support Vector Regression, Kernel Principal Component Analysis, Empirical Results and Implementations

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Journal ArticleDOI

Learning-Assisted Automated Reasoning with Flyspeck

TL;DR: It is shown that 39 % of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation.
Journal ArticleDOI

Automated Reasoning in Higher-Order Logic using the TPTP THF Infrastructure

TL;DR: Key developments have been the specification of the THF language, the addition of higher-order problems to theTPTP, the development of the TPTP THF infrastructure, several ATP systems for higher- order logic, and the use of higher -order ATP in a range of domains.
Journal ArticleDOI

A Revision of the Proof of the Kepler Conjecture

TL;DR: The original Kepler conjecture was published in 2006 as mentioned in this paper, which states that no packing of congruent balls in three-dimensional Euclidean space has density greater than that of the face-centered cubic packing.
Book ChapterDOI

Formal Verification of Floating Point Trigonometric Functions

TL;DR: This paper describes in some depth the formal verification of the sin and cos functions, including the initial range reduction step, covering both pure mathematics and the detailed analysis of floating point rounding.
Book ChapterDOI

Canonical Big Operators

TL;DR: It is shown how these canonical big operations played a crucial enabling role in the study of various parts of linear algebra and multi-dimensional real analysis, as illustrated by the formal proofs of the properties of determinants, of the Cayley-Hamilton theorem and of Kantorovitch's theorem.
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