The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper introduces archetypal dictionaries for a self-taught learning framework for the application of landcover classification. Self-taught learning, an unsupervised representation learning method, is exploited to learn low-dimensional and discriminative higher-level features, which are used as input into a classification algorithm. Experiments are conducted using a multi-spectral Landsat 5 TM...
Deep Belief Network (DBN) is a classic deep learning model, and it can learn higher feature and do better classification job. We combine DBN's basic component Restricted Boltzmann Machines (RBM) with the statistic distribution of Polarimetric SAR (PolSAR) data. Based on it, we develop a deep learning classification method that is suitable for PolSAR data. To verify the effectiveness of the method,...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
In this paper, we propose an algorithm for automatic detection of seals in aerial remote sensing images using features extracted from a pre-trained deep convolutional neural network (CNN). The method consists of three stages: (i) Detection of potential objects, (ii) feature extraction and (iii) classification of potential objects. The first stage is application dependent, with the aim of detecting...
Land Use/Land Cover (LU/LC) of agricultural areas derived from remotely sensed data still remains very challenging. With regard to the rising availability and the improving spatial resolution of satellite data, multitemporal analyses become increasingly important for remote sensing investigations. Even crops with similar spectral behaviour can be separated by adding spectral information of different...
Mapping tree species is an important issue for forest ecosystem services and habitat assessment. In this study, the ability of Formosat-2 multispectral image time series to discriminate thirteen tree species of temperate woodland is investigated. The discrimination is performed using several learning classifiers and testing three levels of classification. The classification accuracies in terms of...
Identification of minimum number of local regions of a handwritten character image, containing well-defined discriminating features which are sufficient for a minimal but complete description of the character is a challenging task. A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here. The powerful framework of Harmony Search has been utilized...
Chatter is an unstable phenomenon in machining processes which brings many detrimental effects to cutting tool and workpiece. This paper proposes an intelligent chatter detection method which is based on wavelet packet node energy (WPNE), least squares support vector machine based recursive feature elimination (LSSVM-RFE) and least squares support vector machine (LSSVM). The method consists of three...
Machine learning classifiers are widely used for text categorization however a classifier misclassifies some of the instances into a category that is relevant to their actual category. The categorization ability of a classifier can be improved by filtering dataset with better classifier and removing such category for misclassified instances. In this paper we proposed a two level approach where level-1...
Huge amount of user request data is generated in web-log. Predicting users' future requests based on previously visited pages is important for web page recommendation, reduction of latency, on-line advertising etc. These applications compromise with prediction accuracy and modelling complexity. we propose a Web Navigation Prediction Framework for webpage Recommendation(WNPWR) which creates and generates...
A number of papers has presented a pattern recognition method for Parkinson's Disease (PD) detection. However, the literatures only able to classify subjects as either healthy of suffering from PD. This paper presents a pattern recognition method for multi stage classification of PD utilizing voice features. 22 features are obtained from University of California-Irvine (UCI) data repository. These...
After lung cancer, breast cancer is known to be the greatest cause for death among females [20]. The improving effectiveness of machine learning approaches is being given a lot of importance by medical practitioners for breast cancer diagnosis. The paper proposes an effective hybridized classifier for breast cancer diagnosis. The classifier is made by combining an unsupervised artificial neural network...
The opinion of other people is often a major factor influencing our decisions. For a consumer it affects purchase decisions and for a producer or a service provider it helps in making business decisions. Companies spend a lot of money and time on surveys for gathering the public opinion on products and services. Now-a-days the web has become a hotspot for finding user opinions on almost anything under...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
Currently, There are many E-commerce websites around the internet world. These E-commerce websites can be categorized into many types which one of them is C2C (Customer to Customer) websites such as eBay and Amazon. The main objective of C2C websites is an online market place that everyone can buy or sell anything at any time. Since, there are a lot of products in the E-commerce websites and each...
This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based...
A Support Vector Machine (SVM) based approach for microgrid islanding decision and control is investigated. The IEEE 13-feeder system is modified and serves as the microgrid model connected to Kundur four-machine two-area system that models the main transmission grid. A representative data set is obtained through simulations in MATLAB/Simulink considering multiple typical scenarios with or without...
This paper introduced a novel forecasting method, Support Vector Regression with Local Predictor (SVRLP), which aims to forecast the short-term load distribution function. To increase the forecast accuracy, the conventional Support Vector Regression (SVR) is combined with a phase space reconstruction technique, called local predictor. This proposed forecast method can be applied to forecast the load...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.