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Multi-label image annotation has received significant attention in the research community over the past few years. Multi-label automatic image annotation assigns keywords to the image based on low level features automatically. In this paper, we present an extensive survey on the research work carried out in the area of multi-label image annotation by using statistical and machine learning approaches...
In this paper, we propose a BSP-Based Support Vector Regression Machine Parallel Framework which can implement the most of distributed Support Vector Regression Machine algorithms. The major difference in these algorithms is the network topology among distributed nodes. Therefore, we adopt the Bulk Synchronous Parallel model to solve the strongly connected graph problem in exchanging support vectors...
State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii)...
Quantitative structure-activity relationship (QSAR) models between tumor necrosis factor a (TNF-a) inhibition activity of lenalidomide analogues and their chemical structures were established by stepwise multiple linear regression (MLR) and support vector machines (SVM) methods. The molecular descriptors of compounds were calculated based on DFT (density functional theory) method, with the basis set...
We present a novel technique for image driven shot retrieval in video data. Specifically, given a query image, our method can efficiently pick the video segment containing that image. Video is first divided into shots. Each shot is described using an embedded hidden Markov model (EHMM). The EHMM is trained on GIST-like descriptors of frames in that shot. The trained EHMM computes the likelihood that...
One-class classification became one of the most challenging research areas of the contemporary machine learning. Contrary to canonical task here we have only information about a single class at our disposal. Therefore more sophisticated methodologies, that are able to handle all the nuisances of the target distribution are required. Fuzzy logic seems an attractive solution to handle imprecision and...
This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chronogait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches...
Parkinson's disease (PD) has been reported to involve postganglionic sympathetic failure and, in 25% of patients, autonomic failure. In this work we investigate autonomic dynamics in PD using a novel methodology able to provide instantaneous estimates of the Lyapunov spectrum within a point process framework.
Most existing surrogate based evolutionary algorithms deal with only one model selected by the authors and different models are not considered. In this paper we propose a framework which enables automatic selection of types of surrogate models, and evaluate the effect of the type of selection on the overall performance of the resulting evolutionary algorithm. Two different types of model selection...
With growing availability and popularity of user generated content, the discipline of sentiment analysis has come to the attention of many researchers. Existing work has mainly focused on either knowledge based methods or standard machine learning techniques. In this paper we investigate sentiment polarity classification based on adaptive statistical data compression models. We evaluate the classification...
Based on L-2 Support Vector Machines(SVMs), Vapnik and Vashist introduced the concept of Learning Using Privileged Information(LUPI). This new paradigm takes into account the elements of human teaching during the process of machine learning. However, with the utilization of privileged information, the extended L-2 SVM model given by Vapnik and Vashist doubles the number of parameters used in the standard...
A system for humanoid robot arm movement concerns about generating a human-like point-to-point (p2p) trajectory. For more than a decade, numerous systems have been devised and many of them were based on complex dynamical systems. In this paper, we introduce a simpler system, integrating support vector machine (SVM) learning model, which can achieve same p2p objective. In our experiment, we compare...
An approach of tempest analysis is introduced and a method of detection and identification of electromagnetic emitting systems by intentionally or unintentionally is applied and discussed. Based on Support Vector Machine(SVM) algorithm, digital signal process method, this paper investigates an primary procedure involving red/black separation and classifying the emission sources radiated from electronics...
Player Modeling tries to model players behaviors and characteristics during a game. When these are related to more abstract preferences, the process is normally called Preference Modeling. In this paper we infer Civilization IV's virtual agents preferences with classifiers based on support vector machines. Our vectors contain score indicators from agents gameplay, allowing us to predict preferences...
In this paper we propose a real time face recognitionmethod that combines face matching and identity verificationmodules in a feedback loop, exploiting the temporalefficiency of matching and the performances of SVM classifiers. Our approach represents an ad-hoc solution for settingscharacterized by variable quantity, quality and distribution oflabeled data among the identities. We assess the procedure...
Transductive support vector machine (TSVM) is one kind of transductive inference process, which combines labeled samples with unlabelled samples to derive the decision rules for classification tasks. Compared with the classical SVM, the transductive SVM is more robust and can achieve better performance. However, there are some disadvantages still being explored. One of the vital drawbacks is its computational...
Reject inference is a term that distinguishes attempts to correct models in view of the characteristics of rejected applicants. The main difficulty in establishing reject inference model is that the ¡®through-the-door' applicant population is unavailable. In this paper, we propose a hybrid data mining technique for reject inference. It is a three-stage approach: k-means cluster, support vector machines...
There are some problems still exist in traditional individual Breast Cancer Diagnosis. To solve the problems, an individual credit assessment model based on support vector classification method is proposed. Using SPSS Clementine data mining tool, the personal credit data is clustering analysis by Support Vector Machine. It is analyzed in detail with the different kernel functions and parameters of...
This paper aims to develop a CO2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE...
Scene classification has been studied extensively in the recent past. Most of the state-of-the-art solutions assumed that scene classes are mutually exclusive. However, this is not true as a scene image may belongs to multiple classes and different people are tend to respond inconsistently even given a same scene image. In this paper, we propose a fuzzy qualitative approach to address this problem...
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