Search results for "Learning"

Development and validation of the team learning questionnaire

2008

Nowadays the organizational scenario is changing in several aspects that affect organization commitment. Team learning construct has emerged as a tool to deal with these changes and the dynamic nature of this situation. Although team learning has acquired importance in recent years, instruments to measure team learning should be developed. The aim of this paper is to develop and validate a team learning scale, the Team Learning Questionnaire, attending to four dimensions of team learning: Continued Improvement Seeking, Dialogue Promotion and Open Communication, Collaborative Learning, and Strategic and Proactive Leadership that Promote Learning. Results provide evidence of the reliability a…

Disabilità ed e-learning

2009

Subjetivación y Feminismo: Análisis de un manifiesto político

2004

The theme of this article is the the Italian feminist movement of the 1980s. That movement was characterised by political transformation and a crtitique of identity. The article takes as its point of departure the inaugural speech of the movement, the manifesto "Piu donne che uomini" (1983). The analysis that we present is inspired in the work of Michel Foucault. It aims to show to that the production of new feministic subjectivities, when fighting the symbolic dimension of masculine domination, has an intrinsically political character. In the 1980s, in the Italian context and particularly in the feminist movement, the rules of the political game set up by the events of 1968 materialized. T…

Collaborative processes during report writing of a science learning project: The nature of discourse as a function of task requirements

2000

The aim of this article is to specify how different aspects of task assignments are related to different types of student discourse during the report writing phase of a science learning project. A group of four ninth-grade students of the Finnish comprehensive school (about 15-year-olds) participated in a project work involving laboratory experiments, reading literature, and analysing and reporting research findings. The empirical data were collected through videotaping and interviews in authentic classroom settings. The results indicated that construction of shared, high-level understanding was quite rare in this case of small group interaction. As one of the main reasons for this, we sugg…

Hierarchies of probabilistic and team FIN-learning

2001

AbstractA FIN-learning machine M receives successive values of the function f it is learning and at some moment outputs a conjecture which should be a correct index of f. FIN learning has two extensions: (1) If M flips fair coins and learns a function with certain probability p, we have FIN〈p〉-learning. (2) When n machines simultaneously try to learn the same function f and at least k of these machines output correct indices of f, we have learning by a [k,n]FIN team. Sometimes a team or a probabilistic learner can simulate another one, if their probabilities p1,p2 (or team success ratios k1/n1,k2/n2) are close enough (Daley et al., in: Valiant, Waranth (Eds.), Proc. 5th Annual Workshop on C…

A formal proof of the ε-optimality of absorbing continuous pursuit algorithms using the theory of regular functions

2014

Published version of an article from the journal: Applied Intelligence. Also available on Springerlink: http://dx.doi.org/10.1007/s10489-014-0541-1 The most difficult part in the design and analysis of Learning Automata (LA) consists of the formal proofs of their convergence accuracies. The mathematical techniques used for the different families (Fixed Structure, Variable Structure, Discretized etc.) are quite distinct. Among the families of LA, Estimator Algorithms (EAs) are certainly the fastest, and within this family, the set of Pursuit algorithms have been considered to be the pioneering schemes. Informally, if the environment is stationary, their ε-optimality is defined as their abili…

Team learning as a game

1997

A machine FIN-learning machine M receives successive values of the function f it is learning; at some point M outputs conjecture which should be a correct index of f. When n machines simultaneously learn the same function f and at least k of these machines outut correct indices of f, we have team learning [k,n]FIN. Papers [DKV92, DK96] show that sometimes a team or a robabilistic learner can simulate another one, if its probability p (or team success ratio k/n) is close enough. On the other hand, there are critical ratios which mae simulation o FIN(p2) by FIN(p1) imossible whenever p2 _< r < p1 or some critical ratio r. Accordingly to [DKV92] the critical ratio closest to 1/2 rom the let is…

Parsimony hierarchies for inductive inference

2004

AbstractFreivalds defined an acceptable programming system independent criterion for learning programs for functions in which the final programs were required to be both correct and “nearly” minimal size. i.e.. within a computable function of being purely minimal size. Kinber showed that this parsimony requirement on final programs limits learning power. However, in scientific inference, parsimony is considered highly desirable. Alim-computable functionis (by definition) one calculable by a total procedure allowed to change its mind finitely many times about its output. Investigated is the possibility of assuaging somewhat the limitation on learning power resulting from requiring parsimonio…

Weighted-Power p Nonlinear Subdivision Schemes

2012

In this paper we present and analyze a generalization of the Powerp subdivision schemes proposed in [3,12]. The Weighted-Powerp schemes are based on a harmonic weighted version of the Power<emp average considered in [12], and their development is motivated by the desire to generalize the nonlinear analysis in [3,5] to interpolatory subdivision schemes with higher than second order accuracy.

Developing and Integrating Advanced Movement Features Improves Automated Classification of Ciliate Species

2015

Recent advances in tracking technologies such as GPS or video tracking systems describe the movement paths of individuals in unprecedented details and are increasingly used in different fields, including ecology. However, extracting information from raw movement data requires advanced analysis techniques, for instance to infer behaviors expressed during a certain period of the recorded trajectory, or gender or species identity in case data is obtained from remote tracking. In this paper, we address how different movement features affect the ability to automatically classify the species identity, using a dataset of unicellular microbes (i.e., ciliates). Previously, morphological attributes a…