Search results for "Learning rule"
showing 8 items of 8 documents
Simple learning rules to cope with changing environments
2008
10 pages; International audience; We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals-one deterministic and the other stochastic. In particular, we examine the influence of the rules' 'learning rate' on the agent's energy gain. We compare the performance of each rule with the best performance attainable when the agent …
Audio-video people recognition system for an intelligent environment
2011
In this paper an audio-video system for intelligent environments with the capability to recognize people is presented. Users are tracked inside the environment and their positions and activities can be logged. Users identities are assessed through a multimodal approach by detecting and recognizing voices and faces through the different cameras and microphones installed in the environment. This approach has been chosen in order to create a flexible and cheap but reliable system, implemented using consumer electronics. Voice features are extracted by a short time cepstrum analysis, and face features are extracted using the eigenfaces technique. The recognition task is solved using the same Su…
A Widrow–Hoff Learning Rule for a Generalization of the Linear Auto-associator
1996
Abstract A generalization of the linear auto-associator that allows for differential importance and nonindependence of both the stimuli and the units has been described previously by Abdi (1988). This model was shown to implement the general linear model of multivariate statistics. In this note, a proof is given that the Widrow–Hoff learning rule can be similarly generalized and that the weight matrix will converge to a generalized pseudo-inverse when the learning parameter is properly chosen. The value of the learning parameter is shown to be dependent only upon the (generalized) eigenvalues of the weight matrix and not upon the eigenvectors themselves. This proof provides a unified framew…
Learning vector quantization with alternative distance criteria
2003
An adaptive algorithm for training of a nearest neighbour (NN) classifier is developed in this paper. This learning rule has some similarity to the well-known LVQ method, but uses the nearest centroid neighbourhood concept to estimate optimal locations of the codebook vectors. The aim of this approach is to improve the performance of the standard LVQ algorithms when using a very small codebook. The behaviour of the learning technique proposed here is experimentally compared to those of the plain k-NN decision rule and the LVQ algorithms.
On the Optimization of Self-Organizing Maps by Genetic Algorithms
1999
Publisher Summary This chapter reviews the research on the genetic optimization of self-organizing maps (SOMs). The optimization of learning rule parameters and of initial weights is able to improve network performance. The latter, however, requires chromosome sizes proportional to the size of the SOM and becomes unwieldy for large networks. The optimization of learning rule structures leads to self-organization processes of character similar to the standard learning rule. A particularly strong potential lies in the optimization of SOM topologies, which allows the study of global dynamical properties of SOMs and related models, as well as to develop tools for their analysis. Hierarchies of …
A Multimodal People Recognition System for an Intelligent Environment
2011
In this paper, a multimodal system for recognizing people in intelligent environments is presented. Users are identified and tracked by detecting and recognizing voices and faces through cameras and microphones spread around the environment. This multimodal approach has been chosen to develop a flexible and cheap though reliable system, implemented through consumer electronics. Voice features are extracted through a short time spectrum analysis, while face features are extracted using the eigenfaces technique. The recognition task is achieved through the use of some Support Vector Machines, one per modality, that learn and classify the features of each person, while bindings between modalit…
Automatic recognition of rapid eye movement (REM) sleep by artificial neural networks.
1995
Artificial neural networks are well known for their good performance in pattern recognition. Their suitability for detecting REM sleep periods on the basis of preprocessed EEG data in humans under clinical conditions was tested and their performance compared with the manual evaluation. A single channel of the EEG signal was analysed in time periods of 20 s and preprocessed into a vector of six real numbers, which served as input to the network. EOG and EMG information was ignored. Backpropagation was used as a learning rule for the network, which consisted of 12 neurons and 39 synapses. Training datasets were put together from the input vectors and the corresponding sleep stages were scored…
Event-Based Trajectory Prediction Using Spiking Neural Networks
2021
International audience; In recent years, event-based sensors have been combined with spiking neural networks (SNNs) to create a new generation of bio-inspired artificial vision systems. These systems can process spatio-temporal data in real time, and are highly energy efficient. In this study, we used a new hybrid event-based camera in conjunction with a multi-layer spiking neural network trained with a spike-timing-dependent plasticity learning rule. We showed that neurons learn from repeated and correlated spatio-temporal patterns in an unsupervised way and become selective to motion features, such as direction and speed. This motion selectivity can then be used to predict ball trajectory…