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In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition. Specifically, we design a new model called Deep Reconstruction-Classification Network (DRCN), which jointly learns a shared encoding representation for two tasks: i) supervised classification of labeled source data, and ii)(More)
This paper describes a new approach to the use of Gaussian distribution in genetic programming (GP) for multiclass object classification problems. Instead of using predefined multiple thresholds to form different regions in the program output space for different classes, this approach uses probabilities of different classes, derived from Gaussian(More)
The crossover operator has been considered "the centre of the storm" in genetic programming (GP). However, many existing GP approaches to object recognition suggest that the standard GP crossover is not sufficiently powerful in producing good child programs due to the totally random choice of the crossover points. To deal with this problem, this paper(More)
Machine learning algorithms such as genetic programming (GP) can evolve biased classifiers when data sets are unbalanced. Data sets are unbalanced when at least one class is represented by only a small number of training examples (called the minority class) while other classes make up the majority. In this scenario, classifiers can have good accuracy on the(More)
" Blendshapes " , a simple linear model of facial expression, is the prevalent approach to realistic facial animation. It has driven animated characters in Hollywood films, and is a standard feature of commercial animation packages. The blendshape approach originated in industry, and became a subject of academic research relatively recently. This course(More)
We propose a simple neural network model to deal with the domain adaptation problem in object recognition. Our model incorporates the Maximum Mean Discrepancy (MMD) measure as a regular-ization in the supervised learning to reduce the distribution mismatch between the source and target domains in the latent space. From experiments , we demonstrate that the(More)
Genetic programming based hyper-heuristics (GPHH) have become popular over the last few years. Most of these proposed GPHH methods have focused on heuristic generation. This study investigates a new application of genetic programming (GP) in the field of hyper-heuristics and proposes a method called GPAM, which employs GP to evolve adaptive mechanisms (AM)(More)