The ever-increasing amount of unstructured data, including text, images, audio, and video, poses a serious challenge to traditional datamining techniques. machinelearning (ML) offers powerful tools and techniques to...
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Support Vector machine(SVM) is one of the most efficient machinelearning algorithms, which is mostly used for patternrecognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech r...
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ISBN:
(纸本)9781479945559
Support Vector machine(SVM) is one of the most efficient machinelearning algorithms, which is mostly used for patternrecognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection. statistics was collected from journals and electronic sources published in the period of 2000 to 2013. patternrecognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector machine ( SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.
The aim of the paper is to present a part of an architecture realized by Huawei, that propose the first Christmas tree endowed with artificial intelligence. Its ability is to identify facial expressions from images ac...
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A challenge for statistical learning is to deal with large data sets, e.g. in datamining. The training time of ordinary Support Vector machines is at least quadratic, which raises a serious research challenge if we w...
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ISBN:
(纸本)354044016X
A challenge for statistical learning is to deal with large data sets, e.g. in datamining. The training time of ordinary Support Vector machines is at least quadratic, which raises a serious research challenge if we want to deal with data sets of millions of examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and paxallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becomes easy to learn an "expert" model separately in each region of the partition. A probabilistic extension and the use of a set of generative models allows representing the gater so that all pieces of the model are locally trained. For SVMs, time complexity appears empirically to local growth linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm probably goes down in a cost function that is an upper bound on the negative log-likelihood.
The proceedings contain 14 papers. The topics discussed include: descriptive analysis of image data: basic models;media analysis and the algorithm ontology;descriptive approach to medical image analysis- substantiatio...
ISBN:
(纸本)9789898111258
The proceedings contain 14 papers. The topics discussed include: descriptive analysis of image data: basic models;media analysis and the algorithm ontology;descriptive approach to medical image analysis- substantiation and interpretation;shape modeling for the analysis of heart deformation patterns;fast multi-view evaluation of data represented by symmetric clusters;search algorithm and the distortion analysis of fine details of real images;a proposal for automatic inference of pressure ulcers grade based on wound images and patient data;an image mining medical warehouse;geo-located image categorization and location recognition;pearling: stroke segmentation with crusted pearl strings;automatic target retrieval in a video surveillance task;and learning probabilistic models for recognizing faces under pose variations.
Multimodal learning, which is simultaneous learning from different data sources such as audio, text, images, is a rapidly emerging field of machinelearning. It is also considered as machinelearning at the next upper...
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In kernel-based machinelearning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed ...
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ISBN:
(纸本)9783642244704;9783642244711
In kernel-based machinelearning algorithms, we can learn a combination of different kernel functions in order to obtain a similarity measure that better matches the underlying problem instead of using a single fixed kernel function. This approach is called multiple kernel learning (MKL). In this paper, we formulate a nonlinear MKL variant and apply it for nuclei classification in tissue microarray images of renal cell carcinoma (RCC). The proposed variant is tested on several feature representations extracted from the automatically segmented nuclei. We compare our results with single-kernel support vector machines trained on each feature representation separately and three linear MKL algorithms from the literature. We demonstrate that our variant obtains more accurate classifiers than competing algorithms for RCC detection by combining information from different feature representations nonlinearly.
The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learningmachine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the...
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ISBN:
(纸本)9789898425980
The latest development (Huang et al., 2011) has shown that better generalization performance can be obtained for extreme learningmachine (ELM) by adding a positive value to the diagonal of HT H or HHT, where H is the hidden layer output matrix. This paper further extends this enhanced ELM to online sequential learning mode. An online sequential learning algorithm is proposed for SLFNs and other regularization networks, consisting of two formulas for two kinds of scenarios: when initial training data is of small scale or large scale. Performance of proposed online sequential learning algorithm is demonstrated through six benchmarking data sets for both regression and multi-class classification problems.
This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector machines (SVM) for cla...
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ISBN:
(纸本)354024509X
This paper presents a new system for recognition, tracking and pose estimation of people in video sequences. It is based on the wavelet transform from the upper body part and uses Support Vector machines (SVM) for classification. recognition is carried out hierarchically by first recognizing people and then individual characters. The characteristic features that best discriminate one person from another are learned automatically. Tracking is solved via a particle filter that utilizes the SVM output and a first order kinematic model to obtain a robust scheme that successfully handles occlusion, different poses and camera zooms. For pose estimation a collection of SVM classifiers is evaluated to detect specific, learned poses.
In recent years, several ontological resources have been proposed to model machinelearning domain. However, they do not provide a direct link to linguistic data. In this paper, we propose a linguistic resource, a set...
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In recent years, several ontological resources have been proposed to model machinelearning domain. However, they do not provide a direct link to linguistic data. In this paper, we propose a linguistic resource, a set of several semantic frames with associated annotated initial corpus in machinelearning domain, we coined MLFrameNet. We have bootstrapped the process of (manual) frame creation by text mining on the set of 1293 articles from the machinelearning Journal from about 100 volumes of the journal. It allowed us to find frequent occurences of words and bigrams serving as candidates for lexical units and frame elements. We bridge the gap between linguistics analysis and formal ontologies by typing the frame elements with semantic types from the DMOP domain ontology. The resulting resource is aimed to facilitate tasks such as knowledge extraction, question answering, summarization etc. in machinelearning domain.
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