Results 11 to 20 of about 8,068,470 (297)
Semi-Supervised Apprenticeship Learning. [PDF]
, 2012 In apprenticeship learning we aim to learn a good policy by observing the behavior of an expert or a set of experts. In particular, we consider the case where the expert acts so as to maximize an unknown reward function defined as a linear combination of a set of state features.
Valko, Michal +2 more
openaire +3 more sources
Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems [PDF]
There has been much interest in applying techniques that incorporate knowledge from unlabelled data into a supervised learning system but less effort has been made to compare the effectiveness of different approaches on real world problems and to ...
Gabrys, Bogdan
core +9 more sources
Semi-supervised learning [PDF]
Machine Learning, 1989 The distribution-independent model of (supervised) concept learning due to Valiant (1984) is extended to that of semi-supervised learning (ss-learning), in which a collection of disjoint concepts is to be simultaneously learned with only partial information concerning concept membership available to the learning algorithm.
Raymond A. Board, Leonard Pitt
openaire +3 more sources
Learning to Learn in a Semi-supervised Fashion [PDF]
, 2020 To address semi-supervised learning from both labeled and unlabeled data, we present a novel meta-learning scheme. We particularly consider that labeled and unlabeled data share disjoint ground truth label sets, which can be seen tasks like in person re-identification or image retrieval.
Yun-Chun Chen +2 more
openaire +3 more sources
Human Semi‐Supervised Learning [PDF]
Topics in Cognitive Science, 2013 AbstractMost empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real‐world learning scenarios, however, are semi‐supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a ...
Bryan R. Gibson +2 more
openaire +2 more sources
Contrastive Semi-Supervised Learning for ASR [PDF]
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models. However, its performance suffers from the supervised teacher model's degrading quality in low-resource setups and under domain transfer. Inspired by the successes of contrastive representation learning for computer vision and speech applications, and ...
Alex Xiao +2 more
openaire +4 more sources
Automatic annotation for weakly supervised learning of detectors [PDF]
, 2012 PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully
Siva, Parthipan
core +4 more sources
Pseudo-Labeling Optimization Based Ensemble Semi-Supervised Soft Sensor in the Process Industry
Sensors, 2021 Nowadays, soft sensor techniques have become promising solutions for enabling real-time estimation of difficult-to-measure quality variables in industrial processes.
Youwei Li +4 more
doaj +1 more source
Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]
Jisuanji kexue, 2022 Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
doaj +1 more source
Semi‐supervised uncorrelated dictionary learning for colour face recognition
IET Computer Vision, 2020 Colour images are increasingly used in the fields of computer vision, pattern recognition and machine learning, since they can provide more identifiable information than greyscale images. Thus, colour face recognition has attracted accumulating attention.
Qian Liu +4 more
doaj +1 more source