AIMC Topic: Classification

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EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

Computational intelligence and neuroscience
Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing ...

Are open set classification methods effective on large-scale datasets?

PloS one
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, deployed classifiers often require the ability to recognize inputs from o...

Adoption of Machine Learning in Intelligent Terrain Classification of Hyperspectral Remote Sensing Images.

Computational intelligence and neuroscience
To overcome the difficulty of automating and intelligently classifying the ground features in remote-sensing hyperspectral images, machine learning methods are gradually introduced into the process of remote-sensing imaging. First, the PaviaU, Botswa...

A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers.

Computational intelligence and neuroscience
Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwi...

Hybrid Low-Order and Higher-Order Graph Convolutional Networks.

Computational intelligence and neuroscience
With the higher-order neighborhood information of a graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher-order graph convolutional networks have a large number of param...

The proteome landscape of the kingdoms of life.

Nature
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported, ...

CHEER: HierarCHical taxonomic classification for viral mEtagEnomic data via deep leaRning.

Methods (San Diego, Calif.)
The fast accumulation of viral metagenomic data has contributed significantly to new RNA virus discovery. However, the short read size, complex composition, and large data size can all make taxonomic analysis difficult. In particular, commonly used a...

TMTCPT: The Tree Method based on the Taxonomic Categorization and the Phylogenetic Tree for fine-grained categorization.

Bio Systems
Fine-grained categorization is one of the most challenging problems in machine vision. Recently, the presented methods have been based on convolutional neural networks, increasing the accuracy of classification very significantly. Inspired by these m...

Weighted discriminative collaborative competitive representation for robust image classification.

Neural networks : the official journal of the International Neural Network Society
Collaborative representation-based classification (CRC) is a famous representation-based classification method in pattern recognition. Recently, many variants of CRC have been designed for many classification tasks with the good classification perfor...

Searching for the Big Pictures.

Perspectives on psychological science : a journal of the Association for Psychological Science
My goal in searching for the big pictures is to discover novel ways of organizing information in psychology that will have both theoretical and practical significance. The first section lists my reasons for writing each of five articles. The second s...