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In this paper, we discuss the importance of feature subset selection methods in machine learning techniques. An analysis of microarray expression was used to check whether global biological differences underlie common pathological features for different types of cancer datasets and to identify genes that might anticipate the clinical behavior of this disease. One way of finding relevant gene selection...
Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified...
Generative models are used in an increasing number of applications that rely on large amounts of contextually rich information about individuals. Owing to possible privacy violations, however, publishing or sharing generative models is not always viable. In this paper, we introduce a novel solution for privately releasing generative models and entire high-dimensional datasets produced by these models...
To construction effective simulation meta-models for complex physical simulation system, the “curse of dimension” and the “uncertain and imprecise information” problems have to be addressed firstly. Although simulation meta-models based on neural networks can obtain well performance, the fuzzy inference mechanism of domain expert for practical application problems cannot be simulated. Thus, some prediction...
In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network...
Sensor based algorithms need to extract features from raw sensor data. However, different devices have different sensor data distributions. This distribution differences lead to a problem that model trained on device A may be invalid when applied to device B. However, it is labor-consuming to collect data and label them on device B from scratch. To solve the problem, a solution is proposed to learn...
Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition...
With the development of algorithms and computer skills, deep learning using CNN (convolutional neural network) has been applied to various fields, especially in image processing field. In this paper, we designed an improved model based on ResNet with CNN structure, and learned the database. The Chaucer Database used in the experiment consisted of 824 Chinese characters among the Chinese characters...
Sparse Modeling Representative Selection (SMRS) has been recently proposed for finding the most relevant instances in datasets. This method deploys a data self-representativeness coding in order to infer a coding matrix that is regularized with a row sparsity constraint. The method assumes that the score of any sample is set to the L2 norm of the corresponding row in the coding matrix. Since the SMRS...
There are quite a few high dimensional time-series data co-ocurring each other such as lip motions, voices, and face appearances and so on. When capturing the correspondent relationships among those time-series data with different dimensionality, we need to make the dimensionality all the same size so that they can be compared each other. To achieve this, Gaussian Process Latent Variable Models (GPLVM)...
We propose a method that uses kernel method-based algorithms to implement an autoencoder. Deep learning-based algorithms have two characteristics, one is the high level data abstraction, the other is the multiple level data transformations and representations. The kernel method is one of the approaches that can be used in linear and non-linear transformations. It should be one of the implementations...
Inspired by recent work in Optical Character Recognition (OCR) and image captioning, an end-to-end system is utilized which implements the recognition of image formulas. An attention based two-way encoder-decoder structure has been proposed to normal image captioning systems, and it achieves good performance on the recognition of image formulas task. This structure together with a new training method...
Aiming at the problem of mine fault prediction, a fault prediction model based on KPCA and Pearson correlation coefficient is proposed. The model obtains the abnormal sampling data by the kernel principal component method, extracts the abnormal sampling data and draws the contribution plots, then the Pearson correlation coefficient is compared with the existing fault contribution plots. Finally, according...
Markov Random Fields are widely used to model lightfield stereo matching problems. However, most previous approaches used fixed parameters and did not adapt to lightfield statistics. Instead, they explored explicit vision cues to provide local adaptability and thus enhanced depth quality. But such additional assumptions could end up confining their applicability, e.g. algorithms designed for dense...
This paper presents the results of using the Least-Squares Support Vector Machines (LS-SVMs) framework for estimating CO2 levels at the Holst Center building in the Netherlands. Within the IoT framework, a Wireless Sensor Network (WSN) consisting of seven sensors is currently deployed at the third floor of the building. Each sensor node provides measures of temperature, relative humidity and CO2 levels,...
In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors over the model parameters and ignore the intrinsic relations among these parameters. Considering the strong complementarity between modalities, we propose a novel joint prior over the...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
A new method of constructing nonparametric dynamic model of the human oculomotor system on the basis of experimental data “input-output” is developed, considering nonlinear and inertial properties of the rectus muscles of the eye. A technology for tracking eye movement is based on the videos. It is possible to determine the dynamic characteristics of the oculomotor system functions as a transition...
Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
Provenance describes detailed information about the history of a piece of data, containing the relationships among elements such as users, processes, jobs, and workflows that contribute to the existence of data. Provenance is key to supporting many data management functionalities that are increasingly important in operations such as identifying data sources, parameters, or assumptions behind a given...
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