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Trajectory-based human activity recognition aims at understanding human behaviors in video sequences. Some existing approaches to this problem, e.g., hidden Markov models (HMM), have a severe limitation, namely the number of motions has to be preset. In fact, this number is difficult to define in advance in real practice. To overcome this shortcoming, we propose a new method for modeling human trajectories...
In this paper, we propose a new learning paradigm named multitask multiclass privileged information support vector machines. The starting point of our work is mainly based on the success of multitask multiclass support vector machines which cast multitask multiclass problems as a constrained optimization problem with a quadratic objective function. Learning using privileged information is an advanced...
In the domain adaptation research, which recently becomes one of the most important research directions in machine learning, source and target domains are with different underlying distributions. In this paper, we propose an ensemble learning framework for domain adaptation. Owing to the distribution differences between source and target domains, the weights in the final model are sensitive to target...
In transfer learning scenarios, previous discriminative dimensionality reduction methods tend to perform poorly owing to the difference between source and target distributions. In such cases, it is unsuitable to only consider discrimination in the low-dimensional source latent space since this would generalize badly to target domains. In this paper, we propose a new dimensionality reduction method...
An adaptive k-nearest neighbor algorithm (AdaNN) is brought forward in this paper to overcome the limitation of the traditional k-nearest neighbor algorithm (kNN) which usually identifies the same number of nearest neighbors for each test example. It is known that the value of k has crucial influence on the performance of the kNN algorithm, and our improved kNN algorithm focuses on finding out the...
Ensemble learning plays an important role in pattern recognition. It combines multiple generated models to solve learning problems, such as classification, regression and feature selection. Existing ensemble methods combine classifiers which are generated and run in parallel. In this paper, a novel ensemble learning approach for semi-supervised learning is proposed. Different from existing ensemble...
For multi-view learning, existing methods usually exploit originally provided features for classifier training, which ignore the latent correlation between different views. In this paper, semantic features integrating information from multiple views are extracted for pattern representation. Canonical correlation analysis is used to learn the representation of semantic spaces where semantic features...
Biometrics based on electroencephalogram (EEG) signals is an emerging research topic. Several recent results have shown its feasibility and potential for personal identification. However, they all use a single task (e.g., signals recorded during imagination of repetitive left hand movements or during resting with eyes open) for classifier design and subsequent identification. In contrast with this,...
Most semi-supervised learning methods assume there are a number of labeled data available in order to learn a classifier which then exploits a large set of unlabeled data. However, for some applications, there are only extremely spare labeled examples attainable (say, one example per category). In this case, these semi-supervised learning methods can not work. In this paper, a new method for seeking...
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
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