In this paper, the basic concepts and survey of the available literature on Support Vector Machines (SVM) in data mining and soft computing research area is provided. While at the time of survey several new methods were found related to SVM like as Support Vector Representation and Discrimination Machine (SVRDM), Recursive SVM (RSVM), On-line Independent SVM (OISVM), Pruning SVM, Fast Nearest N...

During the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce t...

This paper proposes a distance-based kernel change detection algorithm (DKCD). The input vectors from two images of different times are mapped into a potentially much higher dimensional feature space via a nonlinear mapping. Which will usually increase the linear separation of change and no-change regions. Then, a simple linear distance measure between two feature vectors of high dimension is d...

Although computed tomography (CT) has been shown to be useful in diagnosing posterolateral and central lumbar disk herniations, its effectiveness in demonstrating lateral herniated disks has not been emphasized. The myelographic recognition of those herniations may be difficult because root sheaths or dural sacs may not be deformed. A total of 274 CT scans interpreted as showing lumbar disk her...

Hard margin support vector machines (HM-SVMs) have a risk of getting overfitting in the presence of the noise. Soft margin SVMs deal with this problem by the introduction of the capacity control term and obtain the state of the art performance. However, this disposal leads to a relatively high computational cost. In this paper, an alternative method, greedy stagewise algorithm, named GS-SVMs is...

We formulate a framework for soft margin estimation-based linear regression (SMELR) and apply it to supervised speaker adaptation. Enhanced separation capability and increased discriminative ability are two key properties in margin-based discriminative training. For the adaptation process to be able to flexibly utilize any amount of data, we also propose a novel interpolation scheme to linearly...

Hybrid soft computing models, based by neural, fuzzy and evolutionary computation technologies, have been applied to a large number of classification, prediction, and control problems. This paper focuses on one of such applications and presents a systematic process for building a predictive model to estimate time-to-breakage and provide a web break tendency indicator in the wet-end part of pape...

During the past few years, the machine learning community has paid attention to developing new methods for learning from weakly labeled data. This field covers different settings like semi-supervised learning, learning with label proportions, multi-instance learning, noise-tolerant learning, etc. This paper presents a generic framework to deal with these weakly labeled scenarios. We introduce t...

PURPOSE To describe a case of primary eyelid angiosarcoma and review the literature to emphasize aware of this rare disease. CASE REPORT We report a further case which is the first Chinese primary eyelid angiosarcoma in the literature. A 76-year-old woman presented with a 6-month history of a painless lesion on her left eyelid. The patient finally proved to be angiosarcoma on histopathologic ...

Phrase alignment is a crucial step in phrase-based statistical machine translation. We explore a way of improving phrase alignment by adding syntactic information in the form of chunks as soft constraints guided by an in-depth and detailed analysis on a hand-aligned data set. We extend a probabilistic phrase alignment model that extracts phrase pairs by optimizing phrase pair boundaries over th...