60,368 research outputs found
Using fuzzy heterogeneous neural networks to learn a model of the central nervous system control
Fuzzy heterogeneous networks based on similarity are recently introduced feed-forward neural network models composed by neurons of a general class whose inputs are mixtures of continuous (crisp and/or fuzzy) with discrete quantities, admitting also missing data. These networks have activation functions based on similarity relations between inputs and neuron weights. They can be coupled with classical neurons in hybrid network architectures, trained with genetic algorithms. This paper compares the e ectivity of this fuzzy heterogeneous model based on similarity with the classical feed-forward one (scalar-product driven and using crisp quantities) in a time-series prediction setting. The results obtained show a remarkable increasing performance when departing from the classical neuron and a comparable one when confronted with other current powerful techniques, such as the FIR methodology.Peer ReviewedPostprint (author's final draft
Similarity-based heterogeneous neuron models
This paper introduces a general class of neuron models, accepting heterogeneous inputs in the form of mixtures of continuous (crisp or fuzzy) numbers, linguistic information, and discrete (either ordinal or nominal) quantities, with provision also for missing information. Their internal stimulation is based on an explicit similarity relation between the input and weight tuples (which are also heterogeneous). The framework is comprehensive and several models can be derived as instances --in particular, two of the commonly used models are shown to compute a specific similarity function provided all inputs are real-valued and complete. An example family of models defined by composition of a Gower-based similarity with a sigmoid function is shown to lead to network designs (Heterogeneous Neural Networks) capable of learning from non-trivial data sets with a remarkable effectiveness, comparable to that of classical models.Peer ReviewedPostprint (author's final draft
Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search
Despite substantial interest in applications of neural networks to
information retrieval, neural ranking models have only been applied to standard
ad hoc retrieval tasks over web pages and newswire documents. This paper
proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network)
a novel neural ranking model specifically designed for ranking short social
media posts. We identify document length, informal language, and heterogeneous
relevance signals as features that distinguish documents in our domain, and
present a model specifically designed with these characteristics in mind. Our
model uses hierarchical convolutional layers to learn latent semantic
soft-match relevance signals at the character, word, and phrase levels. A
pooling-based similarity measurement layer integrates evidence from multiple
types of matches between the query, the social media post, as well as URLs
contained in the post. Extensive experiments using Twitter data from the TREC
Microblog Tracks 2011--2014 show that our model significantly outperforms prior
feature-based as well and existing neural ranking models. To our best
knowledge, this paper presents the first substantial work tackling search over
social media posts using neural ranking models.Comment: AAAI 2019, 10 page
Fuzzy heterogeneous neural networks for signal forecasting
Fuzzy heterogeneous neural networks are recently introduced models based on neurons accepting heterogeneous inputs (i.e. mixtures of numerical and non-numerical information possibly with missing data) with either crisp or imprecise character, which can be coupled with classical neurons. This paper compares the effectiveness of this kind of networks with time-delay and recurrent architectures that use classical neuron models and training algorithms in a signal forecasting problem, in the context of finding models of the central nervous system controllers.Peer ReviewedPostprint (author's final draft
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