Search results for "learning theory"

Learning from good examples

1995

The usual information in inductive inference for the purposes of learning an unknown recursive function f is the set of all input /output examples (n,f(n)), n ∈ ℕ. In contrast to this approach we show that it is considerably more powerful to work with finite sets of “good” examples even when these good examples are required to be effectively computable. The influence of the underlying numberings, with respect to which the learning problem has to be solved, to the capabilities of inference from good examples is also investigated. It turns out that nonstandard numberings can be much more powerful than Godel numberings.

Coincidence problems for generalized contractions

2014

In this paper, we establish some new existence, uniqueness and Ulam-Hyers stability theorems for coincidence problems for two single-valued mappings. The main results of this paper extend the results presented in O. Mle?ni?e: Existence and Ulam-Hyers stability results for coincidence problems, J. Non-linear Sci. Appl., 6(2013), 108-116. In the last section two examples of application of these results are also given.

Efficacy, Efficiency … and Stability of Intergenerational Programs?

2011

Comparative analysis in terms of computational cost for different discrimination algorithms in implantable defibrillators

2005

Implantable defibrillators (ICDs) use very low computational cost criteria (rate, stability and onset) offering good sensitivity for arrhythmia detection. Although, the specificity of these combined criteria decreases in difficult arrhythmia discrimination as in case of discrimination between ventricular tachycardia (VT) and supraventricular tachycardia (SVT). Several morphological published algorithms enhance arrhythmia discrimination but most algorithms are developed in personal computers and cannot be used in ICDs because of computational cost requirements compared with limited ICD capabilities. A general method to determine the possibility of ICD implementation for a discrimination algo…

A New Unsupervised Neural Network for Pattern Recognition with Spiking Neurons

2006

In this paper we propose a three-layered neural network for binary pattern recognition and memorization. Unlike the classic approach to pattern recognition, our net works organizing itself in an unsupervised way, to distinguish beetween different patterns or to recognize similar ones. If we present a binary input to the first layer, after some time steps we could read the output of the net in the third layer, as one and only one neuron activating with high firing rate; the middle layer will act as a generalization layer, i.e. similar pattern will have similar (or the same) representation in the middle layer. We used learning algorithms inspired from other works or from biological data to ac…

Taking part in Nordic collaboration : nursing students' experiences and perceptions from a learning perspective: a qualitative study

2015

BACKGROUND: Nordic networking of different kinds has a long tradition aiming to increase collaboration and understanding between citizens in different countries. Cultural competence in relation to health care and nursing is important for clinical nurses and is a central issue in nurse education. OBJECTIVE: To gain an understanding of what nurse students experienced and learned during an intensive course in diabetes together with students and nurse educators from Denmark, Finland, Iceland, Norway, Sweden and the Faroe Islands. METHODS: In 2012, an intensive course within the Nordic network, Nordkvist, was conducted in Faroe Islands with the theme "Nursing - to live a good life with diabetes"…

Internal consistency predicts attractiveness in biological motion walkers

2016

Abstract Why do some people appear attractive to us while others don't? Evolutionary psychology states that sexual attractiveness has evolved to assess the reproductive qualities of a potential mate. Past research in the field has identified a number of traits that can be linked directly to qualities such as immuno-competence, developmental stability, and fertility. The current study is motivated by the hypothesis that attractiveness is determined not just by individual, independent traits, but also by whether their pattern is internally consistent. Exploiting the domain of biological motion, we manipulated internal consistency between anthropometry and kinematics of a moving body. In two e…

Opinion dynamics in coalitional games with transferable utilities

2014

This paper studies opinion dynamics in a large number of homogeneous coalitional games with transferable utilities (TU), where the characteristic function is a continuous-time stochastic process. For each game, which we can see as a “small world”, the players share opinions on how to allocate revenues based on the mean-field interactions with the other small worlds. As a result of such mean-field interactions among small worlds, in each game, a central planner allocates revenues based on the extra reward that a coalition has received up to the current time and the extra reward that the same coalition has received in the other games. The paper also studies the convergence and stability of op…

Mapping the Teaching of History of Chemistry in Europe

2016

Recent developments in the field of history of chemistry have introduced new topics, challenges, and connections to a broad range of scientific, political, cultural, economic, and environmental issues. New audiences for the history of chemistry have emerged along with new topics, protagonists, spaces, and historical narratives. This paper summarizes the main results of a recent survey to map the current situation of the teaching of history of chemistry in Europe. We review how and where history of chemistry is taught in Europe, considering not only graduate students in science programs, but also other audiences such as science teachers, and the general public. This paper also provides updat…

Stability-Based Model Selection for High Throughput Genomic Data: An Algorithmic Paradigm

2012

Clustering is one of the most well known activities in scien- tific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is the model selection problem, i.e., the identifi- cation of the correct number of clusters in a dataset. In the last decade, a few novel techniques for model selection, representing a sharp departure from previous ones in statistics, have been proposed and gained promi- nence for microarray data analysis. Among those, the stability-based methods are the most robust and best performing in terms of predic- tion, but the slowest in terms of time. Unfortunately…