Kernel Languages

Kernel methods for learning languages

This paper studies a novel paradigm for learning formal languages from positive and negative examples which consists of mapping strings to an appropriate highdimensional feature space and learning a separating hyperplane in that space. Such mappings can often be represented flexibly with string kernels, with the additional benefit of computational efficiency. The paradigm inspected can thus be ...

A Universal Kernel for Learning Regular Languages

We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but we do have an efficient ǫ-approximation. 1 Background Since the advent of Support Vector Machines (SVMs), kernel methods have flourished in machine learning theory [7]. Formally, a kernel is a positive definite functi...

Cc(m), an Kernel for Implementing Cc Languages

In this article, we tackle the problem of compiling cc(X) languages 9, 13] where X is an admissible constraint domain. The diiculty of this task consists in one) representing and optimising diierent domains X and two) providing an parallel and extensible execution model. A simple way to meet these demands is to provide a minimal parallel (and distributable) kernel language expressive enough to ...

Universal Kernel-Based Learning with Applications to Regular Languages

We propose a novel framework for supervised learning of discrete concepts. Since the 1970’s, the standard computational primitive has been to find the most consistent hypothesis in a given complexity class. In contrast, in this paper we propose a new basic operation: for each pair of input instances, count how many concepts of bounded complexity contain both of them. Our approach maps instances...

Iranian Languages as Gardens