Individual and group tracking with the evaluation of social interactions by Ahmet Yigit, Alptekin Temizel

“…In the learning phase, the discriminative appearance model, consisting of shape, colour and texture features, is extracted and used in AdaBoost online learning. Using the discriminative learning model, state estimation is performed on both individuals and groups. …”
Get full text

Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network by Fangyao Xu, Shaojie Qu

“…Firstly, we put forward some principles to build graphs with a topological structure based on consumption data; secondly, we propose an improved self-attention mechanism model; thirdly, we perform classification tasks related to academic performance, and determine discriminative learning and life behaviour sequence patterns. …”
Get full text

Feature Recalibration in Deep Learning via Depthwise Squeeze and Refinement Operations by Xingpeng Zhang, Xiaohong Zhang

“…Since convolution can discriminately learn the global information of each feature map, we discard the fully connected layer to ensure independence when adjusting the feature map. …”
Get full text

Simultaneous effects of inflectional paradigms and classes in processing of Serbian verbs by Filipović-Đurđević Dušica, Gatarić Isidora

“…We also demonstrate that the effect of Relative entropy on the processing of morphology can arise as a consequence of the principles of discriminative learning in a system that maps input cues to outcomes, with no specification of morphology per se.…”
Get full text

Pyramid Match Kernels: Discriminative Classification with Sets of Image Features (version 2) by Grauman, Kristen, Darrell, Trevor

“…Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. …”
Get full text

Articulation constrained learning with application to speech emotion recognition by Mohit Shah, Ming Tu, Visar Berisha, Chaitali Chakrabarti, Andreas Spanias

“…In this paper, a discriminative learning method for emotion recognition using both articulatory and acoustic information is proposed. …”
Get full text

Deep residual network with regularised fisher framework for detection of melanoma by Nazneen N. Sultana, Bappaditya Mandal, N.B. Puhan

“…Unlike conventional computational methods which require (expensive) domain expertise for segmentation and hand crafted feature computation and/or selection, a deep convolutional neural network‐based regularised discriminant learning framework which extracts low‐dimensional discriminative features for melanoma detection is proposed. …”
Get full text

Object detection utilizing modified auto encoder and convolutional neural networks by Khiarak, Jalil Nourmohammadi, Mazaheri, Samaneh, Tayebi, Rohollah Moosavi, Noorbakhsh-Devlagh, Hamid

“…Some modifications are applied to auto encoder neural networks, for the compact and discriminative learning of object features. Furthermore, for object classification, firstly extracted features are transferred to a convolutional neural network, and after feature convolution with input pictures, they will be classified. …”
Get full text

Comprehension and production of Kinyarwanda verbs in the Discriminative Lexicon by van de Vijver Ruben, Uwambayinema Emmanuel, Chuang Yu-Ying

“…To answer this question we modeled a data set of 11,528 verb forms, hand-annotated for meaning and their grammatical functions, in the Linear Discriminative Learning (LDL), a two-layered, fully connected computational implementation of the Discriminative Lexicon model. …”
Get full text

FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network by Zhaoxuan Liu, Wenjie Luo

“…The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. …”
Get full text

Learning to Rank for Multi-Step Ahead Time-Series Forecasting by Jiuding Duan, Hisashi Kashima

“…Through the lens of the concordance index (CI), we compare the proposed method with conventional regression-based time-series forecasting methods, discriminative learning methods and hybrid methods. Moreover, we discuss the use of the proposed framework for different types of time series and under a variety of conditions. …”
Get full text

Human action recognition using weighted pooling by Wen Zhou, Chunheng Wang, Baihua Xiao, Zhong Zhang

“…To learn the weight, they propose a novel discriminative learning algorithm to capture the discriminative information for pooling operation. …”
Get full text

A Perceptually Inspired New Blind Image Denoising Method Using <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> and Perceptual Loss by A. F. M. Shahab Uddin, Taechoong Chung, Sung-Ho Bae

“…Recently, discriminative learning-based denoising methods have received much attention and have been studied to a large extent because of their high denoising performance with significantly shorter inference time compared to model based denoising methods. …”
Get full text

Compact Dominant Synergistic Excitation Pattern Learning for Illumination-Insensitive Image Representation With Boosting by Tao Gao, Ting Chen, C. C. Wang, Zhanwen Liu, Wei Lu, Y. H. Li

“…Second, a compact DSWEP (C-DSWEP) is learned with a boosted set of weight to generate C-DSWEP codebook. Discriminative learning is aimed at robustness and compactness. …”
Get full text

Robust online learning based on siamese network for ship tracking by Zhongyi Hu, Jingjing Shao, Feiyan Nie, Zhenzhen Luo, Changzu Chen, Lei Xiao

“…Firstly, the algorithm combines the off-line Siamese network classification score and the online classifier score for discriminative learning, and establishes an occlusion determination mechanism according to the classification the fusion score. …”
Get full text

Subspace structural constraint-based discriminative feature learning via nonnegative low rank representation. by Ao Li, Xin Liu, Yanbing Wang, Deyun Chen, Kezheng Lin, Guanglu Sun, Hailong Jiang

“…Feature subspace learning plays a significant role in pattern recognition, and many efforts have been made to generate increasingly discriminative learning models. Recently, several discriminative feature learning methods based on a representation model have been proposed, which have not only attracted considerable attention but also achieved success in practical applications. …”
Get full text

Erythropoietin improves operant conditioning and stability of cognitive performance in mice by Ehrenreich Hannelore, Radyushkin Konstantin, El-Kordi Ahmed

“…During acquisition of this capability, that is, over almost all sequential training phases, learning readouts (magazine training, operant and discriminant learning, stability of performance) were superior in erythropoietin-treated versus control mice.…”
Get full text

Application of Deep Convolutional Generative Adversarial Networks to Generate Pose Invariant Facial Image Synthesis Data by Jagad Nabil Tuah Imanda, Fitra Bachtiar, Achmad Ridok

“…Based on the results of hyperparameter tuning that were performed sequentially, the best hyperparameter combination produced is 200 epoch, 0.002 Generator learning rate, 0.5 Generator momentum/beta1, Adam as Generator optimizer, 0.0002 Discriminator learning rate, 0.5 Discriminator momentum/beta1, and Adam as Discriminator optimizer. …”
Get full text

Semi-supervised generative and discriminative adversarial learning for motor imagery-based brain–computer interface by Wonjun Ko, Eunjin Jeon, Jee Seok Yoon, Heung-Il Suk

“…We focus on problems of small-sized training samples and interpretability of the learned parameters and leverages a semi-supervised generative and discriminative learning framework that effectively utilizes synthesized samples with real samples to discover class-discriminative features. …”
Get full text

Semi-Supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks by Peiyao Yan, Feng He, Yajie Yang, Fei Hu

“…In this paper, we introduced semi-supervised learning into generative adversarial network (GAN), so the discriminator learned more discriminative features from labeled data and unlabeled data. …”
Get full text