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This paper proposes an improved method for DBN, by means of introducing the detachment rate. The introduction of detachment rate can play a similar average role, and can make the complex relationship between the neurons weakened, so that DBN learning has stronger robustness. Three kinds of data (corresponding to healthy, faulted and deteriorating) were classified by the improved depth belief network...
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network. Our model consists of a top-down stack of GANs, each learned to generate lower-level representations conditioned on higher-level representations. A representation discriminator is introduced at each...
We present the fundamental lower bound on dissipation in feedforward neural networks associated with the combined cost of the training and testing phases. Finite state automata descriptions of output generation and the weight updates during training, are used to derive the corresponding lower bounds in a physically grounded manner. The results are illustrated using a simple perceptron learning the...
Understanding the generalization properties of deep learning models is critical for their successful usage in many applications, especially in the regimes where the number of training samples is limited. We study the generalization properties of deep neural networks (DNNs) via the Jacobian matrix of the network. Our analysis is general to arbitrary network structures, types of non-linearities and...
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...
We aimed to study therapeutic effects of antigravity treadmill (AlterG) training on reflex hyper-excitability, muscle stiffness, and corticospinal tract (CST) function in children with spastic hemiplegic cerebral palsy (CP). Three children received AlterG training 3 days per week for 8 weeks as experimental group. Each session lasted 45 minutes. One child as control group received typical occupational...
Since the low SNR environment, generally the modulation recognition rate of signal modulation type is not very high. In this paper, we studied an automatic recognition method of communication signal modulation type in the low SNR. According to analyze the signal entropy as the feature, three characteristics are selected, and the random forest is as the classifier, finally we get a high recognition-rate...
Different data mining techniques are employed in stylometry domain for performing authorship attribution tasks. Sometimes to improve the decision system the discretization of input data can be applied. In many cases such approach allows to obtain better classification results. On the other hand, there were situations in which discretization decreased overall performance of the system. Therefore, the...
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final training on the CIFAR-10 dataset shows that incremental network updates with final training...
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...
Change detection process can be considered as a classification problem, classifying the image into change and no-change classes. Change detection in remote sensing images is an active research area in applications such as disaster management, urban planning etc. Texture is an important characteristic which can be used to identify various objects or regions in an image. Texture features along with...
The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures...
Nowadays, the challenge of learning from large scale and imbalanced data set have attracted a great deal of attention from both industry and academia, which is also deemed to be an important task for fraud detection in telecommunication, finance, online commerce. In general, it's almost impossible to train a classification model on the complete data set, especially in the era of big data, due to the...
This paper proposes a method of reconstructing a scalar field by adaptively choosing sampling locations and using the measurements obtained from those locations to reconstruct an estimate of the underlying field using Gaussian process regression. Spreading sampling points evenly over the field may not always be effective if the field is not uniformly distributed and the maximum number of measurements...
Computer-generated imagery (CGI) is becoming integral to a movie's story and appeal, and even dominates the film's success at box office. Currently the CGI realism is evaluated by post-production supervisors, and few objective realism assessments focus on this area. This paper investigates enhanced feature learning and classifier training for CGI assessment by deep learning. A training-set-selection...
Machine Learning techniques such as Support Vector Machines (SVM) have found applications in many fields, e.g. in Wireless Sensor Networks (WSN) and sensor data processing in general. Especially in the case of WSN energy is very limited as agents solely operate based on battery power after they have been deployed, therefore energy efficiency is of great importance. Furthermore, agents are supposed...
In order to improve the performance of the base classifier in the process of AdaBoost algorithm and simplify the complexity of the whole ensemble learning system, this paper presents a SVM ensemble method based on an improved iteration process of Adaboost algorithm. The improved Adaboost algorithm is added with methods of adding sample selection and feature selection in its iterative process in order...
Kernel methods have been used to effectively tackle nonlinear or nonparametric machine learning problems. However, their computational and memory complexity grows at least quadratically with the number of training samples. This issue has made these methods difficult to use for medium to large-sized datasets and hindered practical applications. A common approach involves the use of only a selected...
With the appearance and development of the technology of malicious codes and other unknown threats, information security has drawn people's attention. In this paper, we investigate on behavior-based detection which is different from traditional static detection technology. Firstly, we discuss the procedure in detail, especially feature extraction and classification. Several machine learning methods...
Neural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection [1] and time series classification [2]. The internal architecture of these...
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