This paper proposes a new and efficient parallel implementation of support vector machines based on decomposition method for handling large scale datasets. The parallelizing is performed on the most time-and-memory consuming work of training, i.e., to update the vector f . The inner problems are dealt by sequential minimal optimization solver. Since the underlying parallelism is realized by the...

A widely-used tool for binary classification is the Support Vector Machine (SVM), a supervised learning technique that finds the “maximum margin” linear separator between the two classes. While SVMs have been well studied in the batch (offline) setting, there is considerably less work on the streaming (online) setting, which requires only a single pass over the data using sub-linear space. Exis...

Future technologies such as Brain-Computer Interaction Technologies (BCIT) or affective Brain Computer Interfaces (aBCI) will need to function in an environment with higher noise and complexity than seen in traditional laboratory settings, and while individuals perform concurrent tasks. In this paper, we describe preliminary results from an experiment in a complex virtual environment. For analy...

Automatic skin lesion detection is a key step in computeraided diagnosis (CAD) of Skin cancers, since the accuracy of the subsequent steps in CAD crucially depends on it. In this paper, a novel method of automatic skin lesion segmentation based on texture analysis and supervised learning is proposed. It firstly involve the clustering of training image into homogeneous regions using Mean-shift; ...

This paper presents a binary classification scheme for investment class rating using support vector machine (SVM). The suggested SVM model is trained offline and takes twelve financial ratios as attributes from different standard investment companies as inputs and correctly classify whether it is a good investment grade or bad investment grade company as output. The overall performance of SVM s...

WHUIR participated in the Temporal Intend Disambiguation (TID) Task of the Temporalia track at NTCIR-12. This paper describes our work of this specific subtask. Given a query, the task is to assign the probability value to four temporal classes i.e. Past, Recency, Future or Atemporal. Our overall strategy has been to rely on established off-the-shelf components (e.g., standard classifiers from ...

We propose a kernel-based online semi-supervised algorithm that is applicable for large scale learning tasks. In particular, we use a multi-view learning framework and a co-agreement strategy to take into account unlabelled data and to improve classification performance of the algorithm. Unlike the standard online methods our algorithm is naturally applicable to many real-world situations where...

Humans use their facial expressions as one of the most effective, quick, and natural ways to convey feelings intentions others. In this research, presents analyses human structure along with its components using Facial Action Units (AUs) Geometric structures for identifying expressions. The approach considers such Nose, Mouth, eyes eye brows FER. Nostril contours left lower tip, right centre ti...

Modern steganalysis is a combination of a feature space design and a supervised binary classification. In this report, we assume that the feature space has been already constructed, i.e., the steganalyst has a set of training features and needs to train a binary classifier. Any machine learning tool can be used for this task and its parameters can be tuned through cross-validation, a standard a...

In the light of general question posed in title, we write down a very simple randomized learning algorithm, based on boosting, that can be seen as nonstationary Markov random process. Surprisingly, decision hyperplanes resulting from this algorithm converge probability to exact hard-margin solutions support vector machines (SVMs). This fact is curious because hyperplane not statistical solution...