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Technology used to hide secret message in a communication called Steganography. Secret message can be text, image or any file that can be converted into binary. This secret message inserted into cover file which can be in form of image, sound or video, basiccaly cover file must be bigger than the secret message in size. Many methods have been proposed on how to hide secret messages in a cover file...
We consider the problem of building accurate and descriptive 3D occupancy maps of an environment from sparse and noisy range sensor data. We seek to accomplish this task by constructing a predictive model online and inferring the occupancy probability of regions we have not directly observed. We propose a novel algorithm leveraging recent advances in data structures for mapping, sparse kernels, and...
In this paper, a novel fast support vector machine (SVM) method combining with the deep quasi-linear kernel (DQLK) learning is proposed for large scale image classification. This method can train large-scale dataset with SVM fast using less memory space and less training time. Since SVM classifiers are constructed by support vectors (SVs) that lie close to the separation boundary, removing the other...
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...
Nonstationary streaming data are characterized by changes in the underlying distribution between subsequent time steps. Learning in such environments becomes even more challenging when labeled data are available only at the initial time step, and the algorithm is provided unlabeled data thereafter, a scenario referred to as extreme verification latency. Our previously introduced COMPOSE framework...
In the classification of high-dimensional hyperspectral images, only spectral information is not sufficient to obtain successful results when the number of training data is small. In this case, spatial information can be exploited as well as spectral information. For this purpose, we aimed to use spatial information obtained from the fuzzy C-means (FCM) algorithm and spectral information together...
In this paper, we propose a novel scheme for domain adaptation in which feature transform and instance weights are jointly optimized. Due to the joint optimization, we can obtain feasible feature transform for domain adaptation while we jointly eliminate source samples which are unrelated to target samples by estimating those weights. By introducing regularization which induces the weights to be homogeneous,...
In this paper we describe a method for nonlinear class-specific discriminant learning that is based on Cholesky Decomposition. We show that the optimization problem solved in Class-Specific Kernel Discriminant Analysis is equivalent to that of Low-Rank Kernel Regression using training data independent target vectors. This connection allows us to devise a new Class-Specific Kernel Discriminant Analysis...
We consider the parameter tuning problem for Gaussian-kernel support vector machines, i.e., how to set its two hyperparameters — σ (bandwidth) and C (tradeoff). Among the many methods in the literature, the majority handle this task by maximizing the cross validation accuracy over the first quadrant of the (σ, C) plane. However, they are all computationally expensive because the objective function...
Deep neural networks are frequently used for computer vision, speech recognition and text processing. The reason is their ability to regress highly nonlinear functions. We present an end-to-end controller for steering autonomous vehicles based on a convolutional neural network (CNN). The deployed framework does not require explicit hand-engineered algorithms for lane detection, object detection or...
For many problems in machine learning fields, the data are nonlinearly distributed. One popular way to tackle this kind of data is training a local kernel machine or a mixture of several locally linear models. However, both of these approaches heavily relies on local information, such as neighbor relations of each data sample, to capture potential data distribution. In this paper, we show the non-local...
Autonomous robots have significant potential for reconnaissance and environmental monitoring applications. Ground robots, in particular, are performing reconnaissance missions in places that are too hazardous for humans. However, these robots are constrained by energy limitations that are impacted by uncertain environments and harsh terrains. The purpose of this work is to develop methods for improving...
In recent years, clasification with hyperspectral images is becoming very popular. Development of camera technology is increasing number of researchers who work in this area. Thanks to hyperspectral imaging technology, specific spectral signatures of objects can be handled. Especially, vegetation clasification is possible by using the spectral information of hyperspectral images. However due to the...
Classification is at the very center of the supervised learning. In this work, we propose a novel algorithm to classify the test data set with the aid of a vector field, emanating from the training data set. In particular, the vector field is constructed such that the location of each training data point becomes a local minimum of the potential. The test data points are allowed to evolve under the...
Support vector machines (SVMs) are promising methods for the prediction of the financial time-series because they use a risk function, consisting of an empirical error and a regularized term, which is derived from the structural risk minimization principle. This study applies SVM for predicting the stock price index. In addition, this study examines the feasibility of the applying SVM in financial...
Deep hierarchical models for feature learning have emerged as an effective technique for object representation and classification in recent years. Though the features learnt using deep models have shown lot of promise towards achieving invariance to data transformations, this primarily comes at the expense of using much larger training data and model size. In the proposed work we devise a novel technique...
In live and studio recordings unexpected sound events often lead to interferences in the signal. For non-stationary interferences, sound source separation techniques can be used to reduce the interference level in the recording. In this context, we present a novel approach combining the strengths of two algorithmic families: NMF and KAM. The recent KAM approach applies robust statistics on frames...
An infrastructure-less indoor localization system is proposed based on fingerprints of light signals acquired at high frequencies. In contrast to other systems that modulate lights, the proposed system distinguishes lights by learning from training samples. Due to slight differences in the electronic components used in the construction of compact fluorescent light (CFL) and light emitting diode (LED)...
Epithelium-stroma classification is always considered as an important preprocessing step for morphological quantitative analysis in image-based histological researches of oncologic diseases. However, large-scale accurate ground-truth labeling is expensive in histopathological image analysis, thus the classification performances will still be limited with the insufficient labeled training samples....
Diffusion maps, when applied to large datasets, are typically constructed by a process of sampling and out-of-sample function extension. However, the performance of anomaly detection in large data when using diffusion maps is sensitive to the chosen samples. In this paper we propose an iterative data-driven approach to improve the sample set and diffusion maps representation. By updating the sample...
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