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Label noise is not uncommon in machine learning applications nowadays and imposes great challenges for many existing classifiers. In this paper we propose a new type of auto-encoder coined label-denoising auto-encoder to learn a representation for robust classification under this situation. For this purpose, we include both the feature and the (noisy) label of a data point in the input layer of the...
Text detection in natural scene images plays an important role in content-based image retrieval, especially user-guided text detection for human-computer interaction. In this paper, we propose a fast and accurate text detection method with user-intention in terms of tap gesture. Firstly, a user-intention slice descriptor is designed based on the estimated text property, which contains all the user...
Speech recognition technology was applied to information collection of agricultural prices, with the acoustic models trained for agricultural prices information collection environment so as to minimize the environmental influence. Firstly, we constructed the speech corpus by collecting speech under the operating scene, and then selected tri-phone modeling as the decode unit to train hidden Markov...
In this paper, we propose to apply the nonparallel support vector machine (NPSVM) for positive and unlabeled learning problem(PU learning problem) in which only a small positive examples and a large unlabeled examples can be used. Like Biased-SVM, NPSVM treats the unlabeled set as the negative set with noise, while NPSVM is modified so that, the first primal problem is constructed such that all the...
Effective dimensionality reduction has been an attractive research area for many large-scale vision and multimedia tasks. Several recent methods attempt to learn optimized graph-based embedding for fast and accurate applications. In this paper, we propose a novel linear unsupervised algorithm, termed Discriminative Partition Sparsity Analysis (DPSA), explicitly considering different probabilistic...
In our previous work, we have investigated the classification of odorants based on their chemical classes only, e.g. Alcohol, Terpene or Ester, using Artificial Neural Networks (ANN) as the signal processing backend of an insect olfactory electronic nose, or e-nose. However, potential applications of e-noses in the food and beverage industry which include the assessment of a fruit's ripeness, quality...
Automatic Image annotation is an important open problem in computer vision. In real world dataset environment, image labels are often noisy. For the task of image annotation with weakly labels, we propose SNLWL, a semantic neighborhood learning model from weakly labeled dataset. Missing labels are replenished using reweighting the error loss function. Then semantic balanced neighborhood is construct...
Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines...
The traditional mathematical modeling is nonrepresentational and it is hard for understanding. In oder to model the real system in a intuitive method, a novel Local BP Neural Network (LBPNN) model has been proposed to imitate arbitrary feed-forward topologies of networks and the weights' training algorithm—constrained stochastic gradient descent (CSGD) is also introduced in this paper. The network...
This paper introduces a discriminative framework for the task of vehicle detection based on Hough Forest. The leaf nodes in Hough Forest framework are not discriminative enough, which means that they do not have the ability to classify whether the test patches ended up in each leaf are positive or negative. Hough votes are assigned to all test patches by Hough forest, including negative test patches,...
A novel identification approach for identifying photographic images (PIM) and photorealistic computer graphics (PRCG) is proposed by using multifractal spectrum features of photo response non-uniformity noise (PRNU). 8 dimensions of mul-tifractal spectrum features of PRNU are extracted to represent the subtle differences between them, and the identification is carried out by using a support vector...
User-provided image tags are usually incomplete or noisy to describe the visual content of corresponding images. In this paper, we consider defective tagging which covers both incomplete and noisy situations, and address the problem of tag completion where tag assignments of training images are defective. While previous studies on tag completion usually assign equal penalty to empirical loss when...
For the purpose of smooth human-robot interaction, a robot is supposed to be capable of semantically parsing the human instructions in a large scale. However, the existing supervised approaches to learning a large-scale semantic parser needs a good deal of training examples with annotations. The exhaustive cost of annotating enough sentences prevents them from learning such parser for interpreting...
Among the OFDM synchronization algorithms, the algorithm based on repeated structured training sequence is widely used because of its simple structure and high accuracy of frequency estimation. An implementation method of OFDM timing synchronization with repeated-structured training sequence is presented in this paper, which uses FPGA as a hardware platform. There are three parts in this design. The...
A space time code based partial rank affine projection (PRAP) algorithm is proposed. The proposed algorithm uses an input signal where the input signal matrix Xk becomes an orthogonal matrix. For this input signal, matrix (XkTXk) becomes a diagonal matrix whose inverse can be easily computed. Thus, the proposed algorithm saves a significant amount of computations. Due to this feature the proposed...
Accurate detection of voids in solder bumps on ball grid arrays (BGAs) is important for improving device quality. Radiographic imaging is commonly used to inspect BGA packages incorporate into LSI circuits. In the case of conventional method, imaging is normally done four times, and the images obtained are averaged to reduce noises. We have developed a nonnegative matrix factorization method for detecting...
In recently, there are many machine learning approaches have developed for intelligent control. One of these approaches is least squares-support vector machine regression (LS-SVMR). Besides, the robustness problem of the LS-SVMR among machine learning algorithms is importantly considered in recent years. Hence, for the robustness problem in LS-SVMR, a least trimmed squares support vector machine regression...
Edge detection is a standard operation in image processing. It becomes problematic if noise is not additive, not Gaussian and not i.i.d. as this happens in images acquired by synthetic aperture radar (SAR). To perform edge detection better, it has been recently proposed to apply a trained neural network (NN) and SAR image pre-filtering for single-look mode. In this paper, we demonstrate that the proposed...
Fusion of ambient breathing signals can be hampered by the current environmental conditions. Fusion methods that are resistant to most adverse ambient conditions may still be susceptible to some. The diversity of multiple fusion methods can be leveraged by employing a variety of fusion methods to fuse each breathing epoch. By using a trained linear classifier to select the best candidate fusion method...
Compressive sensing (CS) has recently attracted lots of attention and has been extended to more structured architectures, for example the linear time-invariant system identification. However, prevalent CS methods used for channel estimation, such as Basis Pursuit Denoising (BPDN) and Dantzig selector (DS), require computational complexity as high as O(N3), where N is the length of the channel. When...
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