Character (mathematics)

About: Character (mathematics) is a(n) research topic. Over the lifetime, 46723 publication(s) have been published within this topic receiving 411412 citation(s).

The mathematics of diffusion

Abstract: Though it incorporates much new material, this new edition preserves the general character of the book in providing a collection of solutions of the equations of diffusion and describing how these solutions may be obtained

Enriching Word Vectors with Subword Information

Abstract: Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models to learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram, words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.

Character theory of finite groups

Abstract: Algebras, modules, and representations Group representations and characters Characters and integrality Products of characters Induced characters Normal subgroups T.I. sets and exceptional characters Brauer's theorem Changing the field The Schur index Projective representations Character degrees Character correspondence Linear groups Changing the characteristic Some character tables Bibliographic notes References Index.

Feature extraction methods for character recognition--a survey

Abstract: This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters The problem of choosing the appropriate feature extraction method for a given application is also discussed When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application