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We introduce a method to greatly reduce the amount of redundant annotations required when crowdsourcing annotations such as bounding boxes, parts, and class labels. For example, if two Mechanical Turkers happen to click on the same pixel location when annotating a part in a given image–an event that is very unlikely to occur by random chance–, it is a strong indication that the...
State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task...
We propose a novel method for temporally pooling frames in a video for the task of human action recognition. The method is motivated by the observation that there are only a small number of frames which, together, contain sufficient information to discriminate an action class present in a video, from the rest. The proposed method learns to pool such discriminative and informative frames, while discarding...
Ensemble methods use multiple classifiers to achieve better decisions than could be achieved using any of the constituent classifiers alone. However, both theoretical and experimental evidence have shown that very large ensembles are not necessarily superior, and small ensembles can often achieve better results. In this paper, we show how to combine a set of weak classifiers into a robust ensemble...
To solve the problem of gradient descent (GD) method which has low accuracy and easily falling into local optimum, the radial basis function (RBF) based on immune algorithm system (IAS-RBF) is proposed. In this method, each antibody is a RBF neural network and the optimal affinity is calculated by immune algorithm system (IAS) to get the best antibody, then the optimal parameter of RBF neural network...
In this paper, an algorithm that based on pca-bp-bagging model is developed for the prediction of pathological data. This algorithm aims at improving the characteristics of bp neural network that the prediction accuracy of pathological data is low, the generalization ability of single bp neural network model is poor, and the anti-interference ability is weak. To enhance the performance of the whole...
A decision tree is an important classification technique in data mining classification. Decision trees have proved to be valuable tools for the classification, description, and generalization of data. J48 is a decision tree algorithm which is used to create classification model. J48 is an open source Java implementation of the C4.5 algorithm in the Weka data mining tool. In this paper, we present...
Implementing higher voltages in vehicles like 48V mild hybrid or full-hybrid enables CO2 reduction and weight savings. However, the increase in the voltage demands an accurate and robust protection system again potential fault conditions. Series arc is one of the fault conditions which needs to be detected and addressed before the benefits of using higher voltages in vehicle can be fully realized...
While there is a large amount of text data on the Internet, people need to organize the text data with experienced category. However, the flat structure of categories could not satisfy the modern information management. To solve this problem, we propose a hierarchical classification process with a strategy, called candidates, used to relieve the blocking problems. Besides, we establish the description...
Click-through rate estimation, the core task of programmatic display advertising, is associated with typical big data problems. Online algorithms for generalized linear models, such as Logistic Regression, are the most widely used data mining techniques for learning at such a massive scale. Since these models are unable to capture the underlying nonlinear data patterns, conjunction features are often...
The basic idea behind the classifier ensembles is to use more than one classifier by expecting to improve the overall accuracy. It is known that the classifier ensembles boost the overall classification performance by depending on two factors namely, individual success of the base learners and diversity. One way of providing diversity is to use the same or different type of base learners. When the...
Current multimodal deep learning approaches rarely explicitly exploit the dependencies inherent in multiple labels, which are crucial for multimodal multi-label classification. In this paper, we propose a multimodal deep learning approach for multi-label classification. Specifically, we introduce deep networks for feature representation learning and construct classifiers with the objective function...
To embed ensemble techniques into belief decision trees for performance improvement, the bagging algorithm is explored. Simple belief decision trees based on entropy intervals extracted from evidential likelihood are constructed as the base classifiers, and a combination of individual trees promises to lead to a better classification accuracy. Requiring no extra querying cost, bagging belief decision...
Tuberculosis is one of the top ten causes of death worldwide. Although this disease is curable and preventable, yet many new tuberculosis cases still occur especially in developing countries. Many low-income families cannot afford the medical diagnosis for tuberculosis. Therefore, this paper proposes an initial screening for tuberculosis infection using a data mining approach. In this paper, the initial...
Aiming at the problem of network security situation prediction, this paper studies the prediction method based on RBF neural network. Through training the RBF neural network, find out the nonlinear mapping relationship between the front N data and the subsequent M data, and then adjust the value of N to explore the different prediction results. The simulation result shows that the proposed method...
This paper investigates the wind speed forecast for the stratospheric airship fixed over a geo-location because the wind speed forecast is a key challenge for the airship station-keeping control. In view of the wind speed series which changes with the time and space and shows the non-linear and non-stationary characteristics, this paper put forward a kind of adaptive model based on Incremental extreme...
This paper introduces a novel open access resource, the machine-readable phonetic dictionary for Romanian — MaRePhoR. It contains over 70,000 word entries, and their manually performed phonetic transcription. The paper describes the dictionary format and statistics, as well as an initial use of the phonetic transcription entries by building a grapheme to phoneme converter based on decision trees....
Activity recognition systems are widely used in monitoring physical and physiological conditions as well as observing the short/long term behavioral patterns for the purpose of improving the health and wellbeing of the users. The major obstacle in widespread use of these systems is the need for collecting labeled data to train the activity recognition model. While a personalized model outperforms...
The pervasive imbalanced class distribution occurring in real-world stream applications, such as surveillance, security and finance, in which data arrive continuously has sparked extensive interest in the study of imbalanced stream classification. In such applications, the evolution of unstable class concepts is always accompanied and complicated by the skewed class distribution. However, most of...
We consider the problem of time-series prediction with missing observations. We consider the autoregressive model (AR model) and cast the problem as a regression problem. On the basic of sampling methods and the online gradient descent (OGD), we propose efficient any-time methods to solve this problem. We show that our algorithm can learn the underlying model efficiently, meanwhile, is robust to the...
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