Search results for " functions"
showing 10 items of 972 documents
Learning by the Process of Elimination
2002
AbstractElimination of potential hypotheses is a fundamental component of many learning processes. In order to understand the nature of elimination, herein we study the following model of learning recursive functions from examples. On any target function, the learning machine has to eliminate all, save one, possible hypotheses such that the missing one correctly describes the target function. It turns out that this type of learning by the process of elimination (elm-learning, for short) can be stronger, weaker or of the same power as usual Gold style learning.While for usual learning any r.e. class of recursive functions can be learned in all of its numberings, this is no longer true for el…
On the duality between mechanistic learners and what it is they learn
1993
All previous work in inductive inference and theoretical machine learning has taken the perspective of looking for a learning algorithm that successfully learns a collection of functions. In this work, we consider the perspective of starting with a set of functions, and considering the collection of learning algorithms that are successful at learning the given functions. Some strong dualities are revealed.
Kernel manifold alignment for domain adaptation
2016
The wealth of sensory data coming from different modalities has opened numerous opportu- nities for data analysis. The data are of increasing volume, complexity and dimensionality, thus calling for new methodological innovations towards multimodal data processing. How- ever, multimodal architectures must rely on models able to adapt to changes in the data dis- tribution. Differences in the density functions can be due to changes in acquisition conditions (pose, illumination), sensors characteristics (number of channels, resolution) or different views (e.g. street level vs. aerial views of a same building). We call these different acquisition modes domains, and refer to the adaptation proble…
Fake Nodes approximation for Magnetic Particle Imaging
2020
Accurately reconstructing functions with discontinuities is the key tool in many bio-imaging applications as, for instance, in Magnetic Particle Imaging (MPI). In this paper, we apply a method for scattered data interpolation, named mapped bases or Fake Nodes approach, which incorporates discontinuities via a suitable mapping function. This technique naturally mitigates the Gibbs phenomenon, as numerical evidence for reconstructing MPI images confirms.
Least-squares community extraction in feature-rich networks using similarity data
2021
We explore a doubly-greedy approach to the issue of community detection in feature-rich networks. According to this approach, both the network and feature data are straightforwardly recovered from the underlying unknown non-overlapping communities, supplied with a center in the feature space and intensity weight(s) over the network each. Our least-squares additive criterion allows us to search for communities one-by-one and to find each community by adding entities one by one. A focus of this paper is that the feature-space data part is converted into a similarity matrix format. The similarity/link values can be used in either of two modes: (a) as measured in the same scale so that one may …
A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.
2020
Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …
Computational issues in fitting joint frailty models for recurrent events with an associated terminal event.
2020
Abstract Background and objective: Joint frailty regression models are intended for the analysis of recurrent event times in the presence of informative drop-outs. They have been proposed for clinical trials to estimate the effect of some treatment on the rate of recurrent heart failure hospitalisations in the presence of drop-outs due to cardiovascular death. Whereas a R-software-package for fitting joint frailty models is available, some technical issues have to be solved in order to use SASⓇ 1 software, which is required in the regulatory environment of clinical trials. Methods: First, we demonstrate how to solve these issues by deriving proper likelihood-decompositions, in particular fo…
Transformations that preserve learnability
1996
We consider transformations (performed by general recursive operators) mapping recursive functions into recursive functions. These transformations can be considered as mapping sets of recursive functions into sets of recursive functions. A transformation is said to be preserving the identification type I, if the transformation always maps I-identifiable sets into I-identifiable sets.
¿Cómo funciona el sistema de innovación del sector cerámico español?
2013
[EN]: In this article we apply the functions of innovation systems framework to assess its appropriateness to characterise the innovation activity of the tile industry in Castellón. This framework is based on idea that a well functioning innovation system requires that a number of key activities take place. If this occurs innovative output is higher. Our analysis provides a deeper understanding of the role of innovation as a strategic option in a mature industry in the context of globalisation. By applying this new theoretical approach to study innovation and highlighting the functions that the system requires, we shown the constraints, inertias, challenges and opportunities that the innova…
A reliable and unbiased human protein network with the disparity filter
2017
AbstractThe living cell operates thanks to an intricate network of protein interactions. Proteins activate, transport, degrade, stabilise and participate in the production of other proteins. As a result, a reliable and systematically generated protein wiring diagram is crucial for a deeper understanding of cellular functions. Unfortunately, current human protein networks are noisy and incomplete. Also, they suffer from both study and technical biases: heavily studied proteins (e.g. those of pharmaceutical interest) are known to be involved in more interactions than proteins described in only a few publications. Here, we use the experimental evidence supporting the interaction between protei…