A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Improving Query Quality for Transductive Learning in Learning to Rank
2020
IEEE Access
Preserved Fulltext
Of course, for scenarios involving no labeled data, the clustering-based transduction method is recommended for generating pseudo-labels for learning to rank. VOLUME XX, 2017 1
C. ...
In this study, the authors presented a semi-supervised learning algorithm that used the clustered-based transductive method combined with a non-measurespecific listwise approach for learning to rank. ...
doi:10.1109/access.2020.3043459
fatcat:pt6w5g7dfvgapndct7prpznmfq
Generating Coherent Summaries with Textual Aspects
2021
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
This paper presents a full-fledged system composed of three modules: finding sentence-level textual aspects, modeling aspect-based coherence with an HMM model, and selecting and ordering sentences with ...
Due to limit of space, we report how our aspect-based HMM model compares with their aspect-agnostic model. ...
Semi-supervised Learning We observe that for this task, classification accuracy may suffer from insufficient training data and a model learned from limited training data may not adapt well to unseen data ...
doi:10.1609/aaai.v26i1.8345
fatcat:rjqlli4xynfopeoxfoiopebm7i
Exploring knowledge of sub-domain in a multi-resolution bootstrapping framework for concept detection in news video
2008
Proceeding of the 16th ACM international conference on Multimedia - MM '08
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, we present a model based on a multi-resolution, multi-source and multi-modal (M3) bootstrapping framework that exploits knowledge of sub-domains for concept detection in news video. ...
Because the characteristics and distributions of data in different sub-domains are different, we model and analyze the video in each sub-domain separately using a transductive framework. ...
For example, Qi et al [16] proposed a transductive learning method to infer unlabeled test data by finding related labeled training data via a clustering method. ...
doi:10.1145/1459359.1459393
dblp:conf/mm/WangCZ08
fatcat:aeqh5tjd4rhr3nf4tqn75nwqwm
Analysis of the IJCNN 2011 UTL challenge
2012
Neural Networks
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The second phase was dedicated to "crosstask transfer learning" (the competitors were provided with a limited amount of labeled data from "source tasks", distinct from the "target tasks"). ...
The goal was to learn data representations that capture regularities of an input space for re-use across tasks. ...
This project is part of the DARPA Deep Learning program and is an activity of the Causality Workbench supported by the Pascal network of excellence funded by the European Commission and by the U.S. ...
doi:10.1016/j.neunet.2012.02.010
pmid:22374109
fatcat:gaoeexsksfaehaf5xaoirg773y
Graph Transduction Learning of Object Proposals for Video Object Segmentation
[chapter]
2015
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Our core contribution is a graph transduction process that learns object proposals densely over space-time, exploiting both appearance models learned from rudimentary detections of sparse objectlike regions ...
By learning a holistic model given a small set of objectlike regions, we propagate this prior knowledge of the recurring primary object to the rest of the video to generate a diverse set of object proposals ...
All clusters are ranked based on the average score S(r) of its comprising regions. ...
doi:10.1007/978-3-319-16817-3_36
fatcat:mrzlxjps6rcwvau33gg23fkfh4
Semi-supervised ranking for document retrieval
2011
Computer Speech and Language
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Despite these successes, most algorithms to date construct ranking functions in a supervised learning setting, which assume that relevance labels are provided by human annotators prior to training the ...
Recently there has been a surge of research in the field of "learning to rank", which aims at using labeled training data and machine learning algorithms to construct reliable ranking functions. ...
Here we will call Problem One "Semi-supervised Rank Learning" and Problem Two "Learning to Rank with Missing Labels". See Figure 2 for a pictorial comparison. ...
doi:10.1016/j.csl.2010.05.002
fatcat:pccdrrrogfe5rjyfjgiogdinhi
Transductive Multilabel Learning via Label Set Propagation
2013
IEEE Transactions on Knowledge and Data Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
In this paper, we study the problem of transductive multilabel learning and propose a novel solution, called TRAsductive Multilabel Classification (TRAM), to effectively assign a set of multiple labels ...
We first formulate the transductive multilabel learning as an optimization problem of estimating label concept compositions. ...
ACKNOWLEDGMENTS The authors wish to thank the editor and anonymous reviewers for their helpful comments and suggestions, and ...
doi:10.1109/tkde.2011.141
fatcat:csa2dtwitvczfcqcwcge33fn4a
Semi-supervised learning to rank with preference regularization
2011
Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2012; you can also visit the original URL.
The file type is application/pdf.
We propose a semi-supervised learning to rank algorithm. It learns from both labeled data (pairwise preferences or absolute labels) and unlabeled data. ...
Learning to Rank challenge 2010. The algorithm runs in linear time in the number of queries, and can work with huge datasets. ...
Acknowledgements We thank Massih Reza Amini for making the semi-supervised RankBoost [2] implementation available, and Tom Minka and Vishwa Vinay for helpful discussions. ...
doi:10.1145/2063576.2063620
dblp:conf/cikm/SzummerY11
fatcat:yrr5hyxn2zel3jhjp3gbtnbyxa
Batch Mode Active Learning based on Multi-set Clustering
2021
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Our method utilizes a sequential active learner to retain the informativeness by providing a ranking of unlabeled samples and constructing multiple informative sets for the subsequent clusterings. ...
Batch mode active learning, where a batch of samples is simultaneously selected and labeled, is a challenging task. ...
Different from typical clustering-based approaches which use a fixed number of samples for clustering, ALMC first applies a clustering model on multiple informative sets and then adopts a selection criterion ...
doi:10.1109/access.2021.3053003
fatcat:h5nmakynrrd2bfbgfc55k5bfdy
Pool-Based Sequential Active Learning for Regression
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Active learning is a machine learning approach for reducing the data labeling effort. ...
This paper focuses on pool-based sequential active learning for regression (ALR). ...
It first builds a base model from a small number of labeled training samples, and then chooses a few most helpful unlabeled samples and queries for their labels. ...
arXiv:1805.04735v1
fatcat:3yws5o2vjvfzxnrnhrh3phkkjm
Effective and Efficient Data Poisoning in Semi-Supervised Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2022 accepted doi:10.1145/3522664.3528606 arXiv:2012.07381v2 fatcat:e5z2taodyzd5tn3cdw5jygqd6q
Semi-Supervised Learning (SSL) aims to maximize the benefits of learning from a limited amount of labelled data together with a vast amount of unlabelled data. ...
Moreover, our method can inform engineers of inputs that deserve investigation (relabelling them) before training the learning model. ...
BACKGROUND AND RELATED WORK Semi-supervised learning [31] is a particular form of machine learning that attempts to maximize the benefits of learning from a limited amount of labelled data together with ...
arXiv:2012.07381v1
fatcat:owbnb7pnlzeb5gncploskfhbbe
Uncertainty sampling and transductive experimental design for active dual supervision
2009
Proceedings of the 26th Annual International Conference on Machine Learning - ICML '09
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models. ...
Empirical studies confirm the potential of these schemes to significantly reduce the cost of acquiring labeled data for training high-quality models. ...
Acknowledgements We thank Yan Liu and Kai Yu for helpful conversations. We thank Gregory Druck for providing datasets for comparison with GE-FL. ...
doi:10.1145/1553374.1553496
dblp:conf/icml/SindhwaniML09
fatcat:iqdvno3c2vcxnnkeutfmkv6dzu
Intelligent Biometric Information Management
2010
Intelligent Information Management
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. ...
It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised learning, and Information Theory (IY) using ...
An expanded framework that integrates graph-based semi-supervised learning and spectral clustering for the purpose of grouping and classification, i.e., label propagation, can be developed. ...
doi:10.4236/iim.2010.29060
fatcat:zp7qyfpwdzdo7dbsek2hkdgj2i
A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals
2019
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. ...
The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. ...
In order to address the above issues, a transductive model is proposed by combining transductive learning with clustering methods based on neighborhood knowledge in high dimension, as shown in Figure ...
doi:10.3390/s19020429
fatcat:onewqqphavbrtlx4wr4kqepxnq
Active Concept Learning in Image Databases
2005
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
To achieve concept learning, we a) propose a new user directed semi-supervised expectation-maximization algorithm for mixture parameter estimation, and b) develop a novel model selection method based on ...
This paper presents an active concept learning approach based on the mixture model to deal with the two basic aspects of a database system: the changing (image insertion or removal) nature of a database ...
For unsupervised learning, it is usually impossible to achieve satisfactory estimation for mixture model based on such limited number of samples in the high-dimensional feature space. ...
doi:10.1109/tsmcb.2005.846653
pmid:15971914
fatcat:prrpcpeqh5gbtfqpeecrsyjyti
« Previous
Showing results 1 — 15 out of 5,665 results