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We propose EC3, a novel algorithm that merges classification and clustering together in order to support both binary and multi-class classification. EC3 is based on a principled combination of multiple classification and multiple clustering methods using a convex optimization function. We additionally propose iEC3, a variant of EC3 that handles imbalanced training data. We perform an extensive experimental...
The rapid development of high-throughput sequencing technology provides unique opportunities for studies of transcription factor binding, while also bringing new computational challenges. Recently, a series of discriminative motif discovery (DMD) methods have been proposed and offer promising solutions for addressing these challenges. However, because of the huge computational cost, most of them have...
Segment Routing (SR) can be used as a traffic engineering strategy to counteract increasing loads on networks like Internet Service Provider (ISP) backbones. Many SR approaches, however, optimize traffic flows that were measured in the past. This paper introduces a new tunnel training architecture. It aims to show that the results of these strategies can still be beneficial for routing new traffic...
Multi-objective optimization plays an important role when one has fitness functions that are somehow conflicting with each other. Also, parameter-dependent machine learning techniques can benefit from such optimization tools. In this paper, we propose a multi-objective-based strategy approach to build compact though representative training sets for Optimum-Path Forest (OPF) learning purposes. Although...
Proactive caching is a promising technology in 5G wireless networks. Small-cell base stations (SBS) can cache popular contents to assist the macro base station, and proactive caching are considered to cope with the weak backhaul links of SBSs. However, obtaining popular contents and making the optimal caching strategy may be challenging. In this paper, a novel learning-based approach is proposed,...
Motivated by the capability of sparse coding based anomaly detection, we propose a Temporally-coherent Sparse Coding (TSC) where we enforce similar neighbouring frames be encoded with similar reconstruction coefficients. Then we map the TSC with a special type of stacked Recurrent Neural Network (sRNN). By taking advantage of sRNN in learning all parameters simultaneously, the nontrivial hyper-parameter...
A novel projection twin support vector machine (PTSVM), termed as NPTSVM, is presented in this paper for binary classification. Although this method determines two projection vectors using the same way as PTSVM, it has more advantages than existing PTSVMs. First, NPTSVM does not have to calculate inverse matrices during the learning process, which makes the training speed of NPTSVM be much faster...
Neural networks have demonstrated promising results for a wide range of applications. The proposed techniques employ different architectures and objective functions to adapt to the application while enabling a feasible implementation. Commonly used objective functions for network optimization are based on the cross entropy between the empirical distribution of the training data and the model distribution...
In this paper, a self-learning approach is proposed towards solving scene-specific pedestrian detection problem without any human annotation involved. The self-learning approach is deployed as progressive steps of object discovery, object enforcement, and label propagation. In the learning procedure, object locations in each frame are treated as latent variables that are solved with a progressive...
Support vector machine (SVM) is a popular machine learning method and has been widely applied in many real-world applications. Since SVM is sensitive to noises, fuzzy SVM (FSVM) has been proposed to relieve the over-fitting problem caused by noises through assigning a fuzzy membership to each sample. Then, different samples make different contributions to the learning of classification hyperplane...
In this paper, Multi-Task Linear Dependency Modeling is proposed to distinguish drug-related webpages that contain lots of images and text. Linear Dependency Modeling exploits semantic relations between images features and text features, and Multi-Task Learning takes advantage of metadata of webpages. Meaningful information of webpages can be made use of fully to improve classification accuracy. Experimental...
Automatic Image Annotation (AIA) plays an important role in large-scaled intelligent image management and retrieval. Based on the correlation between image low-level features and high-level semantic concepts, images can be efficiently retrieved from large-scaled image dataset. Recently, many researchers leverage machine learning techniques to annotate images automatically. However, these methods still...
There are two types of data in semi-supervised learning: feature vector with the corresponding label and feature vector without label, where labeled data processing has been well studied in supervised learning. In this paper, we derive the LogSumExp function for unlabeled data processing. This derivation establishes a unified view of labeled data processing and unlabeled data processing in semi-supervised...
We propose a sequential algorithm for learning sparse radial basis approximations for streaming data. The initial phase of the algorithm formulates the RBF training as a convex optimization problem with an objective function on the expansion weights while the data fitting problem imposed only as an ℓ∞-norm constraint. Each new data point observed is tested for feasibility, i.e., whether the data fitting...
Semi-Supervised Support Vector Machines (S3VMs) have been proposed to deal with the proliferation of partially labelled data available in many large-scale complex systems. Since most existing S3VMs do not correspond to convex problems nor have practical solutions especially on large imbalanced data, this limits their utility in practice. In this work, we propose an efficient approach to the semi-supervised...
Optimization is important in neural networks to iteratively update weights for pattern classification. Existing optimization techniques suffer from suboptimal local minima and slow convergence rate. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) algorithm is proposed to optimize neural network weights using multi-stage backpropagation manner. SDAGD is derived from the operation...
Various optimal power flow (OPF) algorithms have been utilized in power networks primarily for cost reduction, profit optimization and/or system loss minimization. However, these optimization solvers are either computationally demanding or sensitive to initialization settings and do not guarantee a global optimal. In order to overcome these challenges, this paper proposes an artificial intelligent...
We study a single location supply system for repairable spare parts. The system consists of a multi-server repairshop and inventory of ready-to-use spare parts. When a failed part is received, a new (or as-good-as-new) replacement (if on stock) is sent back, and the failed part is queued for repair. In the case of unavailability, the failure requests are fulfilled when a ready-for-use part is received...
Currently, the research on recommend system has drawn intensive attention from many researchers. However, it is still a concerned focus to make accurate recommendations in a short time period. Recently, some researchers have proposed a formal model based on learning to rank for Top-N recommendation. Aiming at improving the effect of Top-N recommendation and solving the problem of timeliness, we analyze...
Dimensionality reduction plays an important role in solving the “curse of the dimensionality” and attracts a number of researchers in the past decades. In this paper, we proposed a new supervised linear dimensionality reduction method named largest center-specific margin (LCM) based on the intuition that after linear transformation, the distances between the points and their corresponding class centers...
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