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Found 15 papers, 7 papers with code
ARTxAI: Explainable Artificial Intelligence Curates Deep Representation Learning for Artistic Images using Fuzzy Techniques
We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.
The Concept of Semantic Value in Social Network Analysis: an Application to Comparative Mythology
We use these concept of meaning and semantic affinity to analyze and compare the gods and heroes from three different classical mythologies: Greek, Celtic and Nordic.
Towards interval uncertainty propagation control in bivariate aggregation processes and the introduction of width-limited interval-valued overlap functions
Overlap functions are a class of aggregation functions that measure the overlapping degree between two values.
Neuro-inspired edge feature fusion using Choquet integrals
In this work we elaborate on the fusion of early vision primitives using generalizations of the Choquet integral, and novel aggregation operators that have been extensively studied in recent years.
A fusion method for multi-valued data
In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data.
Motor-Imagery-Based Brain Computer Interface using Signal Derivation and Aggregation Functions
In BCI applications, the ElectroEncephaloGraphy is a very popular measurement for brain dynamics because of its non-invasive nature.
Ranked #1 on
EEG 4 classes
on BCI Competition IV 2a
Interval-valued aggregation functions based on moderate deviations applied to Motor-Imagery-Based Brain Computer Interface
In this work we study the use of moderate deviation functions to measure similarity and dissimilarity among a set of given interval-valued data.
Ranked #3 on
EEG Left/Right hand
on BCI Competition IV 2a
Learning ordered pooling weights in image classification
We present a method to learn the weights of the OWA aggregation operator in a Bag-of-Words framework and in Convolutional Neural Networks, and provide an extensive evaluation showing that OWA based pooling outperforms classical aggregation operators.
Co-occurrence of deep convolutional features for image search
The feature map from the last convolutional layer of a CNN encodes descriptive information from which a discriminative global descriptor can be obtained.
Adaptive binarization based on fuzzy integrals
This document presents a new adaptive binarization technique based on fuzzy integral images through an efficient design of a modified SAT for fuzzy integrals.
Do we still need fuzzy classifiers for Small Data in the Era of Big Data?
Our findings show that, although slightly inferior, Big Data classifiers are gradually catching up with state-of-the-art classifiers for Small data, suggesting that a unified learning algorithm for Big and Small Data might be possible.
On the usage of the probability integral transform to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems
We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems.
CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems
However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments.
Generalized Interval-valued OWA Operators with Interval Weights Derived from Interval-valued Overlap Functions
In this work we extend to the interval-valued setting the notion of an overlap functions and we discuss a method which makes use of interval-valued overlap functions for constructing OWA operators with interval-valued weights.
Optical images-based edge detection in Synthetic Aperture Radar images
We compare the modified and unmodified versions of the gravitational edge detection technique with the well-established one proposed by Canny, as well as with a recent multiscale fuzzy-based technique proposed by Lopez-Molina et Alejandro We also address the issues of aggregation of gray level images before and after edge detection and of filtering.
