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The imbalance problem exists in P300 EEG data sets because P300 potential are collected under the condition of Oddball experimental paradigm. Hence, a P300 detection method, namely RUSBagging SVMs, is proposed in this paper to solve the imbalance problem and make an improvement. This algorithm re-samples the data sets at first to generate a rebalanced training set in one round of iteration and trains...
This paper proposes the implementation of a Support Vector Machines (SVM) for automatic recognition of numerical speech commands. Besides the pre-processing of the speech signal with Mel Frequency Ceptral Coefficients (MFCC), is used to Discrete Cosine Transform (DCT) to generate a two-dimensional matrix used as input to SVM algorithm for generating the pattern of words to be recognized. The Support...
In many real-world applications such as image classification, labeled training examples are difficult to obtain while unlabeled examples are readily available. In this context, semi-supervised learning methods take advantage of both labeled and unlabeled examples. In this paper, a greedy graph-based semi-supervised learning (GGSL) approach is proposed for multi-class classification problems. The labels...
This paper proposes an improved SVM based multi-label classification method by using relationship among labels. Following a traditional multi-label solution, binary relevance (BR) method is first used to decompose the multi-label classification problem into multiple binary classification sub-problems, each of which is solved by an SVM classifier. By using Platt's sigmoid technique, each SVM classifier...
This paper presents a geometrical approach for obtaining large margin classifiers. The method aims at exploring the geometrical properties of the dataset from the structure of a Gabriel graph, which represents pattern relations according to a given distance metric, such as the Euclidean distance. Once the graph is generated, geometric vectors, analogous to SVM's support vectors are obtained in order...
Deep learning methods allow a classifier to learn features automatically through multiple layers of training. In a deep learning process, low-level features are abstracted into high-level features. In this paper, we propose a new probabilistic deep learning method that combines a discriminative model, namely, Support Vector Machine (SVM), with a generative model, namely, Gaussian Mixture Model (GMM)...
This paper presents a Transductive Support Vector Machine (TSVM) with quasi-linear kernel based on a clustering assumption for semi-supervised classification. Since the potential separating boundary is located in low density area between classes, a modified density clustering method by considering label information is firstly introduced to extract the information of potential separating boundary in...
The identification of predictive biomarkers of complex disease with robustness and specificity is an ongoing challenge. Gene expressions provide information on how the cell reacts to a particular state and the relationship of genes may lead to novel information. A network-based approach integrating expression data with protein-protein interaction network can be used to identify gene-subnetwork biomarkers...
In some practical classification problems in which the number of instances of a particular class is much lower/higher than the instances of the other classes, one commonly adopted strategy is to train the classifier over a small, balanced portion of the training data set. Although straightforward, this procedure may discard instances that could be important for the better discrimination of the classes,...
Similarity learning ranges over an extensive field in machine learning and pattern recognition. This paper deals with similarity learning based on multiple support vector data description (SVDD). It is well known that SVDD was proposed for one-class or two-class unbalanced learning problems. Thus, we propose a multiple SVDD (MSVDD) algorithm and apply it to multi-class learning problems. A SVDD model...
A major problem in a brain-computer interface (BCI) based on electroencephalogram (EEG) recordings is the varying statistical properties of the signals during inter- or intra-session transfers that often lead to deteriorated BCI performances. A filter bank CSP (FBCSP) algorithm typically uses all the features from all the bands to extract and select robust features. In this paper, we evaluate the...
Open set recognition is, more than an interesting research subject, a component of various machine learning applications which is sometimes neglected: it is not unusual the existence of learning systems developed on the top of closed-set assumptions, ignoring the error risk involved in a prediction. This risk is strictly related to the location in feature space where the prediction has to be made,...
In this paper we investigate optical flow field features for the automatic labeling of word prominence. Visual motion is a rich source of information. Modifying the articulatory parameters to raise the prominence of a segment of an utterance, is usually accompanied by a stronger movement of mouth and head compared to a non-prominent segment. One way to describe such motion is to use optical flow fields.
Recent years the distributed representations of words (i.e., word embeddings) have been shown to be able to significantly improve performance in many natural language processing tasks, such as pos-of-tag tagging, chunking, named entity recognition and sentiment polarity judgement, etc. However, previous tasks only involve a single sentence. In contrast, this paper evaluates the effectiveness of word...
Accurate weather forecasting is one of most challenging tasks that deals with a large amount of observations and features. In this paper, a black-box modeling technique is proposed for temperature forecasting. Due to the high dimensionality of data, feature selection is done in two steps with k-Nearest Neighbors and Elastic net. Next, Least Squares Support Vector Machine regression is applied to generate...
There is a growing interest in data-analytic modeling for prediction and/or detection of epileptic seizures from EEG recording of brain activity [1–10]. Even though there is clear evidence that many patients have changes in EEG signal prior to seizures, development of robust seizure prediction methods remains elusive [1]. We argue that the main issue for development of effective EEG-based predictive...
If a robot is expected to perform in the real-world, the robot should recognize objects in such environment using its multimodal sensors in real-time. Traditional multimodal object classification methods focus on recognizing known objects; however, it is impossible to learn all objects that we use. On the other hand, the classification of unknown objects has become a popular topic in image processing...
Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of...
Dialogue act recognition is recognized as an important step for computers to understand human dialogues as it is closely related to the human intention. There are two main challenges in dialogue act recognition. Firstly, multimodal features should be taken into consideration, which include lexical, syntactic, prosodic cues, even facial appearance and gesture. Secondly, samples distribution in the...
The probabilistic classification vector machine is a very effective and generic probabilistic and sparse classifier. A recently published incremental version improved the runtime complexity to quadratic costs. We derive the Nyström approximation for asymmetric matrices to obtain linear runtime and memory complexity for the incremental probabilistic classification vector machine while keeping similar...
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