Search results for "Machine"
showing 10 items of 2592 documents
Prototype-based learning on concept-drifting data streams
2014
Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To ide…
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties – A review
2015
Abstract: Forthcoming superspectral satellite missions dedicated to land monitoring, as well as planned imaging spectrometers, will unleash an unprecedented data stream. The processing requirements for such large data streams involve processing techniques enabling the spatio-temporally explicit quantification of vegetation properties. Typically retrieval must be accurate, robust and fast. Hence, there is a strict requirement to identify next-generation bio-geophysical variable retrieval algorithms which can be molded into an operational processing chain. This paper offers a review of state-of-the-art retrieval methods for quantitative terrestrial bio-geophysical variable extraction using op…
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
2014
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about "what-is-happening-now" with a negligible delay. The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized. To perform real-time analytics, pre-processing of data should be performed in a way that only a short summary of stream is stored in main memory. In addition, due to high speed of arrival, average processing time for each instance of data should be in such a way that…
Sequential Learning with LS-SVM for Large-Scale Data Sets
2006
We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to approximate the full kernel on arbitrary points. This subset is identified online from the data stream. We improve upon existing approaches (esp. the kernel recursive least squares algorithm) by proposing a new, supervised criterion for the selection of the relevant basis functions that takes into account the approximation error incurred from approximating the kernel as well as the reduction of the cost in th…
Migration Techniques in HPC Environments
2014
Process migration is an important feature in modern computing centers as it allows for a more efficient use and maintenance of hardware. Especially in virtualized infrastructures it is successfully exploited by schemes for load balancing and energy efficiency. One can divide the tools and techniques into three groups: Process-level migration, virtual machine migration, and container-based migration.
Local dimensionality reduction and supervised learning within natural clusters for biomedical data analysis
2006
Inductive learning systems were successfully applied in a number of medical domains. Nevertheless, the effective use of these systems often requires data preprocessing before applying a learning algorithm. This is especially important for multidimensional heterogeneous data presented by a large number of features of different types. Dimensionality reduction (DR) is one commonly applied approach. The goal of this paper is to study the impact of natural clustering--clustering according to expert domain knowledge--on DR for supervised learning (SL) in the area of antibiotic resistance. We compare several data-mining strategies that apply DR by means of feature extraction or feature selection w…
A dynamic integration algorithm for an ensemble of classifiers
1999
Numerous data mining methods have recently been developed, and there is often a need to select the most appropriate data mining method or methods. The method selection can be done statically or dynamically. Dynamic selection takes into account characteristics of a new instance and usually results in higher classification accuracy. We discuss a dynamic integration algorithm for an ensemble of classifiers. Our algorithm is a new variation of the stacked generalization method and is based on the basic assumption that each basic classifier is best inside certain subareas of the application domain. The algorithm includes two main phases: a learning phase, which collects information about the qua…
Inferring Decision Strategies from Clickstreams in Decision Support Systems: A New Process-Tracing Approach using State Machines
2011
The importance of online shopping has grown remarkably over the last decade. In 2009, every West European spent on average € 483 online and this amount is expected to grow to € 601 in 2014. In Germany, the number of online shoppers has almost doubled since 2000: 44% of all adults regularly buy products onlinetoday. In Western Europe, online sales reached € 68 billion in 2009 and Forrester research forecasts it will reach € 114 billion by 2014 with an 11% compound annual growth rate.
Verbal ordinal classification with multicriteria decision aiding
2008
Abstract Professionals in neuropsychology usually perform diagnoses of patients’ behaviour in a verbal rather than in a numerical form. This fact generates interest in decision support systems that process verbal data. It also motivates us to develop methods for the classification of such data. In this paper, we describe ways of aiding classification of a discrete set of objects, evaluated on set of criteria that may have verbal estimations, into ordered decision classes. In some situations, there is no explicit additional information available, while in others it is possible to order the criteria lexicographically. We consider both of these cases. The proposed Dichotomic Classification (DC…
Deep Learning Techniques for Depression Assessment
2018
Depression is a typical mood disorder, which affects a significant number of individuals worldwide at an increasing rate. Objective measures for early detection of signs related to depression could be beneficial for clinicians with regards to a decision support system. In this paper, assessment of depression is done by applying three deep learning techniques of Convolutional Neural Network (CNN). These techniques are transfer learning using AlexNet, fine-tuning using AlexNet and building an end to end CNN. The inputs of the CNNs are a combination of Motion History Image, Landmark Motion History Image and Gabor Motion History Image, and have been generated on a depression dataset. Accuracy o…