Repositório Colecção: Livros de ActasLivros de Actashttps://hdl.handle.net/1822/12672024-03-29T13:35:21Z2024-03-29T13:35:21ZImplementing Bayes’ rule with neural fieldsCuijpers, Raymond H.Erlhagen, Wolframhttps://hdl.handle.net/1822/109542017-12-15T16:11:35Z2010-10-21T15:32:53ZTítulo: Implementing Bayes’ rule with neural fields
Autor: Cuijpers, Raymond H.; Erlhagen, Wolfram
Resumo: Bayesian statistics is has been very successful in describing behavioural data on decision making and cue integration under noisy circumstances. However, it is still an open question how the human brain actually incorporates this functionality. Here we compare three ways in which Bayes rule can be implemented using neural fields. The result is a truly dynamic framework that can easily be extended by non-Bayesian mechanisms such as learning and memory.
<b>Tipo</b>: conferencePaper2010-10-21T15:32:53ZOn the development of intention understanding for joint action tasksErlhagen, WolframMukovskiy, AlbertChersi, FabianBicho, E.https://hdl.handle.net/1822/109532017-12-15T16:11:35Z2010-10-21T14:30:47ZTítulo: On the development of intention understanding for joint action tasks
Autor: Erlhagen, Wolfram; Mukovskiy, Albert; Chersi, Fabian; Bicho, E.
Resumo: Our everyday, common sense ability to discern the intentions of others’ from their motions is fundamental for a successful cooperation in joint action tasks. In this paper we address in a modeling study the question of how the ability to understand complex goal-directed action sequences may develop
during learning and practice. The model architecture reflects recent neurophysiological findings that suggest the existence of chains of mirror neurons associated with specific goals.
These chains may be activated by external events to simulate the consequences of observed actions. Using the mathematical
framework of dynamical neural fields to model the dynamics of different neural populations representing goals, action means
and contextual cues, we show that such chains may develop based on a local, Hebbian learning rule. We validate the
functionality of the learned model in a joint action task in which an observer robot infers the intention of a partner to chose a complementary action sequence.
<b>Tipo</b>: conferencePaper2010-10-21T14:30:47ZMisturas de regressões lineares: um novo teste de alteração da estruturaFaria, SusanaSoromenho, Gildahttps://hdl.handle.net/1822/57382018-06-28T11:20:18Z2006-11-02T11:29:19ZTítulo: Misturas de regressões lineares: um novo teste de alteração da estrutura
Autor: Faria, Susana; Soromenho, Gilda
Resumo: Neste trabalho, descrevemos um teste de alteração da estrutura que desenvolvemos para o caso de modelos de mistura de regressões lineares. Este teste baseia-se na comparação entre o modelo de mistura estimado a partir do conjunto de observações iniciais e o modelo de mistura estimado a partir da totalidade das observações disponíveis (observações iniciais e bservações novas).
Com o objectivo de ilustrar a aplicação deste teste em situações práticas onde as misturas de regressões lineares são adequadas, apresentam-se alguns exemplos de aplicação recorrendo a amostras geradas.
<b>Tipo</b>: conferenceAbstract2006-11-02T11:29:19ZA non parametric robust method for the detection of outliers in linear modelsFaria, SusanaMelfi, Giuseppehttps://hdl.handle.net/1822/57282018-06-28T11:19:50Z2006-10-26T15:14:56ZTítulo: A non parametric robust method for the detection of outliers in linear models
Autor: Faria, Susana; Melfi, Giuseppe
Resumo: The detection of outliers for the standard least squares regression is a problem which has been extensevily studied. Lad Regression diagnostics offers alternative approaches whose main feature is the robustness. In this work, we propose a nonparametric method for detecting outliers in LAD regression models and compare t to other classical methods.
<b>Tipo</b>: conferenceAbstract2006-10-26T15:14:56ZDétection non-paramétrique robuste d'observations aberrantes et à effet de LevierMelfi, GiuseppeFaria, Susanahttps://hdl.handle.net/1822/44592017-10-04T15:50:06Z2006-02-17T18:09:10ZTítulo: Détection non-paramétrique robuste d'observations aberrantes et à effet de Levier
Autor: Melfi, Giuseppe; Faria, Susana
Resumo: La détection d'observations aberrantes et à effet de levier selon la méthode des moindre carrés est un probléme qui a été largement étudié.; The detection of influential observations for the standard least squares
regression model is a problem which has been extensively studied. LAD regression diagnostics
offers alternative approaches whose main feature is the robustness. Here a
nonparametric method for detecting influencial observations is presented and compared
with other classical diagnostics methods.
<b>Tipo</b>: conferencePaper2006-02-17T18:09:10Z