Stratified and time-aware sampling based adaptive ensemble learning for streaming recommendations
Recommender systems have played an increasingly important role in providing users with tailored suggestions based on their preferences. However, the conventional offline recommender systems cannot handle the ubiquitous data stream well. To address ...
A hybrid discriminant embedding with feature selection: application to image categorization
In recent times, feature extraction was the focus of many researches due to its usefulness in the machine learning and pattern recognition fields. Feature extraction mainly aims to extract informative representations from the original set of ...
An effective dynamic spatiotemporal framework with external features information for traffic prediction
Traffic prediction is necessary for management departments to dispatch vehicles and for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve ...
Collaborative attention neural network for multi-domain sentiment classification
Multi-domain sentiment classification is a challenging topic in natural language processing, where data from multiple domains are applied to improve the performance of classification. Recently, it has been demonstrated that attention neural ...
Modeling low- and high-order feature interactions with FM and self-attention network
Click-Through Rate (CTR) prediction has always been a very popular topic. In many online applications, such as online advertising and product recommendation, a small increase in CTR will bring great returns. However, CTR prediction has always ...
Hierarchical correlation siamese network for real-time object tracking
Under the influence of deep learning, many trackers have emerged recently. Among them, Siamese network reaches a pleasant balance between accuracy and speed, but its tracking performance still lags behind other trackers. In this paper, we have ...
A co-training method based on entropy and multi-criteria
Co-training method is a branch of semi-supervised learning, which improves the performance of classifier through the complementary effect of two views. In co-training algorithm, the selection of unlabeled data often adopts the high confidence ...
A new efficient decision making algorithm based on interval-valued fuzzy soft set
Interval-valued fuzzy soft set is an extended model of soft set. It is a new mathematical tool that has great advantages in dealing with uncertain information and is proposed by combining soft sets and interval-valued fuzzy sets. The two existing ...
Mixture of experts with convolutional and variational autoencoders for anomaly detection
This study focused on the problem of anomaly detection (AD) by means of mixture-of-experts network. Most of the existing AD methods solely based on the reconstruction errors or latent representation using a single low-dimensional manifold are ...
Feature selection based on term frequency deviation rate for text classification
Feature selection is a technique to select a subset of the most relevant features for modeling training. In this paper, a new concept of TDR is firstly proposed to improve the classification accuracy. Then, a TDR-based algorithm for text ...
Novel metaheuristic based on multiverse theory for optimization problems in emerging systems
Finding an optimal solution for emerging cyber physical systems (CPS) for better efficiency and robustness is one of the major issues. Meta-heuristic is emerging as a promising field of study for solving various optimization problems applicable to ...
A many-objective optimized task allocation scheduling model in cloud computing
The characteristics of randomness, running style, and unpredictability of user requirements in the cloud environment, brings great challenges to task scheduling. Meanwhile, the scheduling efficiency of cloud task allocation is an important factor ...
Mask-guided SSD for small-object detection
Detecting small objects is a challenging job for the single-shot multibox detector (SSD) model due to the limited information contained in features and complex background interference. Here, we increased the performance of the SSD for detecting ...
Application of incremental support vector regression based on optimal training subset and improved particle swarm optimization algorithm in real-time sensor fault diagnosis
Attracted by the advantages of support vector regression and incremental learning approach, it is proposed in this work that an incremental support vector regression (ISVR) model optimized by particle swarm optimization (PSO) algorithm, and some ...
Sentiment analysis for customer relationship management: an incremental learning approach
In recent years there has been a significant rethinking of corporate management, which is increasingly based on customer orientation principles. As a matter of fact, customer relationship management processes and systems are ever more popular and ...
Recommending content using side information
Collaborative Filtering methods predict user interests and make recommendations just by using the rating matrix. However, in practice there is extensive side information about users and items, such as the age of the user, the actors in a movie, or ...
Multi-label classification by formulating label-specific features from simultaneous instance level and feature level
Multi-label learning (MLL) trains a classification model from multiple labelled datasets, where each training instance is annotated with a set of class labels simultaneously. Following the binary relevance MLL paradigm, a recently effective spirit ...
Lung cancer detection using enhanced segmentation accuracy
Lung cancer is currently one of the most common causes of cancer-related death. Detecting and providing an accurate diagnosis of potentially cancerous lung nodules at an early stage of their development would increase treatment efficacy and so ...
An adaptive adjustment strategy for bolt posture errors based on an improved reinforcement learning algorithm
Designing an intelligent and autonomous system remains a great challenge in the assembly field. Most reinforcement learning (RL) methods are applied to experiments with relatively small state spaces. However, the complicated situation and high-...
A new multi-task learning method with universum data
Multi-task learning (MTL) obtains a better classifier than single-task learning (STL) by sharing information between tasks within the multi-task models. Most existing multi-task learning models only focus on the data of the target tasks during ...
Low-rank and sparse matrix factorization with prior relations for recommender systems
The explosive growth of data has caused users to spend considerable time and effort finding the items they need. Various recommender systems have been created to provide convenience for users. This paper proposes a low-rank and sparse matrix ...
STA-Net: spatial-temporal attention network for video salient object detection
This paper conducts a systematic study on the role of spatial and temporal attention mechanism in the video salient object detection (VSOD) task. We present a two-stage spatial-temporal attention network, named STA-Net, which makes two major ...
Research on image Inpainting algorithm of improved GAN based on two-discriminations networks
All existing image inpainting methods based on neural network models are affected by structural distortions and blurred textures on visible connectivity, such that overfitting and overlearning phenomena can easily emerge in the image inpainting ...
Batch mode active learning via adaptive criteria weights
Batch mode active learning (BMAL) is absorbed in training reliable classifier with deficient labeled examples by efficiently querying the most valuable unlabeled examples for supervision. In particular, BMAL always selects examples based on the ...
Correction to: Batch mode active learning via adaptive criteria weights
A correction to this paper has been published: https://doi.org/10.1007/s10489-020-02146-9
Multi-scale spatiotemporal graph convolution network for air quality prediction
Air pollution is a serious environmental problem that has attracted much attention. Air quality prediction can provide useful information for urban environmental governance decision-making and residents’ daily health control. However, existing ...
Usr-mtl: an unsupervised sentence representation learning framework with multi-task learning
Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation ...
An improved aspect-category sentiment analysis model for text sentiment analysis based on RoBERTa
The aspect-category sentiment analysis can provide more and deeper information than the document-level sentiment analysis, because it aims to predict the sentiment polarities of different aspect categories in the same text. The main challenge of ...
Multilinear subspace learning using handcrafted and deep features for face kinship verification in the wild
In this paper, we propose a new multilinear and multiview subspace learning method called Tensor Cross-view Quadratic Discriminant Analysis for face kinship verification in the wild. Most of the existing multilinear subspace learning methods ...
Clothing fashion style recognition with design issue graph
Fashion style recognition of clothing images facilitates the clothing retrieval and recommendation in E-commerce. It is still a challenging task because the clothing images of same style may have diverse visual appearances. Existing fashion style ...
