The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive...
The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients...
This article presents the objectives of the project, Training of Trainers in Robotics for Schools in Vulnerable Areas of Costa Rica as well as the main activities, and the results that have been obtained in its first phase of execution. This is a joint project of the School of Informatics of the National University of Costa Rica (UNA), the Costa Rican Institute on Drugs (ICD) and the Ministry of Public...
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that...
This paper presents a trajectory generation mechanism based on machine learning for a network of unmanned aerial vehicles (UAVs). For delay compensation, we apply an online regression technique to learn a pattern of network-induced effects on UAV maneuvers. Due to online learning, the control system not only adapts to changes to the environment, but also maintains a fixed amount of training data....
In Acoustic Scene Classification (ASC) two major approaches have been followed. While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted...
Background Modelling is a crucial step in background/foreground detection which could be used in video analysis, such as surveillance, people counting, face detection and pose estimation. Most methods need to choose the hyper parameters manually or use ground truth background masks (GT). In this work, we present an unsupervised deep background (BG) modelling method called BM-Unet which is based on...
We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights...
In this paper we propose to use an adaptive ensemble learning framework with different levels of diversity to handle streams of data in non-stationary scenarios in which concept drifts are present. Our adaptive system consists of two ensembles, each one with a different level of diversity (from high to low), and, therefore, with different and complementary capabilities, that are adaptively combined...
At present, machine learning is widely used for classification, such as automatic speech recognition, image identification, text classification and numbers of researches for fault diagnosis besides. Generally, most of the models used for fault diagnosis are based on the same data distribution, while the applications of the equipment in actual production and operation are mostly under unstable conditions,...
Nowadays, fault detect and prediction is quite important for the purpose of ensuring the correct functioning of complex system; nevertheless, it is usually difficult to establish an exact mathematical model in analytical form for complex system, therefore, fault prediction of complex system always relays on the analysis of the observed chaotic time series. In order to enhance the validity and accuracy...
This paper proposes a novel ensemble method to improve the performance of binary classification. The proposed method is a non-linear combination of base models and an application of adaptive selection of the most suitable model for each data instance. Ensemble methods, an important type of machine learning technique, have drawn a lot of attention in both academic research and practical applications,...
Radio tomographic imaging (RTI) is an emerging technique of device-free localization (DFL). The main challenge of RTI is the multipath interferences in RSS measurements, which could make the links become more unpredictable and finally lead to unsatisfactory DFL performance. For addressing this challenge, this paper presents a novel modeling method based on relevance vector machine (RVM), which can...
Active exercise can improve the rehabilitation efficacy of the paralyzed patients, while we need to consider its safety. Impedance control takes the active compliance into account, providing a safe and comfortable training environment for the patients. However, the traditional impedance control has poor robustness, so it is urgent to enhance the adaptive capability of the system. This paper proposes...
This paper is devoted to real-time analysis of continuous footwork training routine in fencing. We propose a model-based adaptive filtering algorithm for accurate selection of segments of interest from a velocity signal acquired by the Kinect motion sensor. We remove false positives from the selected segments by extracting dedicated features and applying a SVM classifier. Finally, we compute parameters...
CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through fine-tuning a fixed-size network on new target data. Indeed, virtually every contemporary...
Recent advances have enabled oracle classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers are relatively heavyweight, so that applying them to classify video is costly. We show that day-to-day video exhibits highly skewed class distributions over the short term, and that these distributions can be classified by much...
In linear representation-based image classification, an unlabeled sample is represented by the entire training set. To obtain a stable and discriminative solution, regularization on the vector of representation coefficients is necessary. For example, the representation in sparse representation-based classification (SRC) uses L1 norm penalty as regularization, which is equal to lasso. However, lasso...
Multi-task learning aims to improve generalization performance of multiple prediction tasks by appropriately sharing relevant information across them. In the context of deep neural networks, this idea is often realized by hand-designed network architectures with layers that are shared across tasks and branches that encode task-specific features. However, the space of possible multi-task deep architectures...
Person re-identification is an open and challenging problem in computer vision. Existing approaches have concentrated on either designing the best feature representation or learning optimal matching metrics in a static setting where the number of cameras are fixed in a network. Most approaches have neglected the dynamic and open world nature of the re-identification problem, where a new camera may...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.