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Domain Generalization for Object Recognition with Multi-task Autoencoders
TLDR
This work proposes a new feature learning algorithm, Multi-Task Autoencoder (MTAE), that provides good generalization performance for cross-domain object recognition and evaluates the performance of the algorithm on benchmark image recognition datasets, where the task is to learn features from multiple datasets and to then predict the image label from unseen datasets.
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation
TLDR
A new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and unsupervised reconstruction of unlabeled target data, is designed.
A Survey on Evolutionary Computation Approaches to Feature Selection
TLDR
This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms.
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization
TLDR
This paper proposes Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization and shows that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach
TLDR
The experimental results show that the two PSO-based multi-objective algorithms can automatically evolve a set of nondominated solutions and the first algorithm outperforms the two conventional methods, the single objective method, and the two-stage algorithm.
Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms
TLDR
Experiments on twenty benchmark datasets show that PSO with the new initialisation strategies and/or the new updating mechanisms can automatically evolve a feature subset with a smaller number of features and higher classification performance than using all features.
Evolving Deep Convolutional Neural Networks for Image Classification
TLDR
A new method using genetic algorithms for evolving the architectures and connection weight initialization values of a deep convolutional neural network to address image classification problems and a novel fitness evaluation method is proposed to speed up the heuristic search with substantially less computational resource.
Automated Design of Production Scheduling Heuristics: A Review
TLDR
The state-of-the-art approaches are summarized, suggesting a taxonomy, and providing the interested researchers and practitioners with guidelines for the design of hyper-heuristics in production scheduling are summarized and suggested.
Developing New Fitness Functions in Genetic Programming for Classification With Unbalanced Data
TLDR
This paper aims to both highlight the limitations of the current GP approaches in this area and develop several new fitness functions for binary classification with unbalanced data and empirically show that these new Fitness functions evolve classifiers with good performance on both the minority and majority classes.
Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data
TLDR
Experimental results on six (binary) class imbalance problems show that the evolved ensembles outperform their individual members, as well as single-predictor methods such as canonical GP, naive Bayes, and support vector machines, on highly unbalanced tasks.
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