Computational learning of natural language is often attempted without using the knowledge available from other research areas such as psychology and linguistics. This can lead to systems that solve problems that are neither theoretically or practically useful. In this paper we present a system CLL which aims to learn natural language syntax in a way that is both computationally effective and ps...

The kernel method is a popular approach to analyzing structured data such as sequences, trees, and graphs; however, unordered trees have not been investigated extensively. Kimura et al. (2011) proposed a kernel function for unordered trees on the basis of their subpaths, which are vertical substructures of trees responsible for hierarchical information in them. Their kernel exhibits practically...

We prove that perfect distributions exist when using a finite number of bits to represent the parameters of a Bayesian network. In addition, we provide an upper bound on the probability of sampling a non-perfect distribution when using a fixed number of bits for the parameters and that the upper bound approaches zero exponentially fast as one increases the number of bits. We also provide an upp...

The decentralisation of Swedish healthcare closer to citizens has been slow. Drawing from empirical material the reform prior and amidst COVID-19 pandemic, this paper argues that pandemic disrupted ecosystem. Consequently, citizen-centred collaborations have accelerated integration resources (such as knowledge skills) across organisational, hierarchical professional borders. However, delimited ...

We describe, analyze, and evaluate experimentally a new probabilistic model for word-sequence prediction in natural language based on prediction suffix trees (PSTs). By using efficient data structures, we extend the notion of PST to unbounded vocabularies. We also show how to use a Bayesian approach based on recursive priors over all possible PSTs to efficiently maintain tree mixtures. These mi...