Smoothing and compression with stochastic -testable tree languages

Smoothing and compression with stochastic k-testable tree languages

In this paper, we describe some techniques to learn probabilistic k-testable tree models, a generalization of the well known k-gram models, that can be used to compress or classify structured data. These models are easy to infer from samples and allow for incremental updates. Moreover, as shown here, backing-off schemes can be defined to solve data sparseness, a problem that often arises when u...

Stochastic k-testable Tree Languages and Applications

Probabilistic k-Testable Tree Languages

Tree Languages Arithmetic Compression

In this paper, we explore the appli ability to ompression tasks of the algorithms for regular language inferen e from sto hasti samples. We ompare two arithmeti en oders based upon two di erent kinds of formal languages: string languages and tree languages. The experiments show that tree-based methods outperform the predi tive apability of stringbased methods when they are applied to les ontain...

A Decidable Characterization of Locally Testable Tree Languages

A regular tree language L is locally testable if membership of a tree in L depends only on the presence or absence of some fix set of neighborhoods in the tree. In this paper we show that it is decidable whether a regular tree language is locally testable. The decidability is shown for ranked trees and for unranked unordered trees.