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The classification performances of the traditional one-class support vector machine (OCSVM) and its variants are often not satisfying when outliers are complex. To deal with this case, assigning smaller weights to these outliers may alleviate their influence upon the classification boundary and enhance the robustness of OCSVM. In this paper, a novel adaptive-weighted one-class support vector machine...
Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different...
We propose a scheme for training a neural network as an image classifier. The approach includes a very rapid unsupervised feature learning algorithm and a supervised technique. We show that convolving and downsampling clustered descriptors of image patches with each input image can provide more discriminative features compared to both pre-trained descriptors and randomly generated convolutional filters...
Aspect-based sentiment analysis has always been a difficult task since it consists of several core sub-tasks: feature detection, opinion extraction and polarity classification. Consequently, by now there is little work to summarize all of these works together. In this paper, we propose a brand new holistic system, which can deal with all the problems above simultaneously using aspect-based positive...
A novel and efficient Dendrite Ellipsoidal Neuron based on hyper-ellipsoids is proposed. By using the clustering algorithm k-means++, the method automatically sets an optimum number of dendrites and increases classification performance. The proposed network overcomes the actual Dendrite Morphological Neural Networks due to it changes hyper-boxes by hyper-ellipsoids that create smoother decision boundaries...
In recent years, the research on neural networks has been guided by the search of new mathematical frameworks, with the hope of finding new features, as geometric interpretation, for facing today problems or reducing the computational cost. In this paper we introduce a new Clifford Neuron [1], extending the conformai neuron, presented in [2] through the generalization of the geometric algebra of quadratic...
In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously...
Nonstationary streaming data are characterized by changes in the underlying distribution between subsequent time steps. Learning in such environments becomes even more challenging when labeled data are available only at the initial time step, and the algorithm is provided unlabeled data thereafter, a scenario referred to as extreme verification latency. Our previously introduced COMPOSE framework...
A generative model based on training deep architectures is proposed. The model consists of K networks that are trained together to learn the underlying distribution of a given data set. The process starts with dividing the input data into K clusters and feeding each of them into a separate network. After few iterations of training networks separately, we use an EM-like algorithm to train the networks...
This paper presents the results of language clustering in the i-vectors space, a method to determine in an unsupervised manner how many languages are in a data set and which recordings contain the same language. The most dense i-vectors clusters are found using the DBSCAN algorithm in a low dimensional space obtained by the t-SNE method. Quality of clustering for spherical k-means and the proposed...
Cross-domain learning text classification aims to train an accurate model for a target domain by using labeled text data from a source domain with different but related data distributions. To narrow the data distribution gap between different domains, most of the previous approaches utilize the bag-of-words model to obtain latent features representation of the text. However, this kind of model loses...
In practice, there are a variety of real-world datasets that have an imbalanced nature where one of two classes dominates the data. These datasets are generally difficult to classify using machine learning algorithms as the skewed nature of the data has a significant impact on the training process. In order to combat this difficulty, many methods of under sampling and over sampling have been proposed...
In practice, unlabeled data can be cheaply and easily collected from target domain, but it is quite difficult and expensive to obtain a large amount of labeled data. Therefore how to use both of labeled and unlabeled data to improve the learning performance becomes critical issue for many real-world applications. Active Learning and Semi-supervised Learning are right solutions to such problem, and...
In this study, we propose an ensemble learning architecture called "Cognitive Learner", for classification of cognitive states from functional magnetic resonance imaging (fMRI). Proposed architecture consists of a two-layer hierarchy. In the first layer, called voxel layer, we model the connectivity among the voxel time series to represent the detailed information about the experiment. In...
Because of the popularity of Internet and mobile Internet, people are facing serious information overloading problems nowadays. Recommendation engine is very useful to help people to reach the Internet news they want through the network. Collaborative filtering (CF), such as item-based CF, is the most popular branch in recommendation domain. But the data's high-dimension as well as data sparsity are...
In this research, we consider the related problem of malware classification based on HMMs. We train HMMs for a variety of malware generators and a variety of compilers. The results of HMM are further classified using k means algorithm but k means algorithm has drawback of stuck into local minima so we optimized the k means with genetic algorithm (GA). Genetic algorithm (GA) tuned k means clustering...
Credit scoring plays an important role in financial institutions and debt based crowdfunding platforms as well as peer to peer lending platforms. In the last few years, adopting ensemble methods for credit scoring has become much more popular. However, the performance of ensemble methods is easily affected by the parameter settings and the number of base classifiers. Ensemble classification based...
The Nearest Neighbor Classification (NNC) has been widely used as classification method, due to its simplicity, classification efficiency and its ability to deal with different classification problems. Despite its good classification accuracy, the NNC suffers from many shortcomings on the execution time, noise sensitivity, high storage requirements and lack of interpretability. In this paper, we propose...
In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic...
Gene Regulatory Network (GRN) represents the regulatory interactions between genes. Experimental methods are capable of determining the nature of gene regulation in a given system, but are time-consuming and expensive. High-throughput technologies produce large number of gene expression data. These data along with additional information from heterogeneous data sources enable computational biologists...
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