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Online Crowdsourcing Subjective Image Quality Assessment
Subjective Image Quality Assessment Online Crowdsourc- ing Paired Comparison
2016/1/25
Recently, HodgeRank on random graphs has been proposed as an effective framework for multimedia quality assessment problem based on paired comparison method. With the ran-dom design on large graphs, i...
Online Crowdsourcing Subjective Image Quality Assessment
Subjective Image Quality Assessment Online Crowdsourc- ing Paired Comparison
2016/1/20
Recently, HodgeRank on random graphs has been proposed as an effective framework for multimedia quality assessment problem based on paired comparison method. With the ran-dom design on large graphs, i...
We introduce online learning algorithms which are independent of feature scales, proving regret bounds dependent on the ratio of scales existent in the data rather than the absolute scale. This has se...
Online Learning in a Contract Selection Problem
Online Learning Contract Selection Problem
2013/6/14
In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitab...
On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
Generalization Ability Online Learning Algorithms Pairwise Loss Functions
2013/6/14
In this paper, we study the generalization properties of online learning based stochastic methods for supervised learning problems where the loss function is dependent on more than one training sample...
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
Online Learning Markov Decision Processes Adversarially Chosen Transition Probability Distributions
2013/5/2
We study the problem of learning Markov decision processes with finite state and action spaces when the transition probability distributions and loss functions are chosen adversarially and are allowed...
Second-Order Non-Stationary Online Learning for Regression
Second-Order Non-Stationary Online Learning for Regression
2013/4/28
The goal of a learner, in standard online learning, is to have the cumulative loss not much larger compared with the best-performing function from some fixed class. Numerous algorithms were shown to h...
We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Speci cally if the sequence encountered by the learner is described well by a know...
Behavior patterns of online users and the effect on information filtering
bipartite networks reshuffling process information filtering
2011/7/19
Understanding the structure and evolution of web-based user-object bipartite networks is an important task since they play a fundamental role in online information filtering. In this paper, we focus o...
Efficient Online Learning via Randomized Rounding
Efficient Online Learning Randomized Rounding
2011/7/6
Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader.
Online algorithms for Nonnegative Matrix Factorization with the Itakura-Saito divergence
Online algorithms Nonnegative Matrix Factorization Itakura-Saito divergence
2011/7/6
Nonnegative matrix factorization (NMF) is now a common tool for audio source separation.
Adaptive and Optimal Online Linear Regression on L1-balls
online linear regression indi-vidual sequences Adaptive Optimal
2011/6/20
We consider the problem of online linear regression on indi-
vidual sequences. The goal in this paper is for the forecaster to output
sequential predictions which are, after T time rounds, almost as...
Online Multiple Kernel Learning for Structured Prediction
Online Multiple Kernel Learning r Structured Prediction
2010/10/19
Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning...
Creating a loyal customer base is one of the most important, and at the same time, most difficult tasks a company faces. Creating loyalty online (e-loyalty) is especially difficult since customers ca...
Security Analysis of Online Centroid Anomaly Detection
Security Analysis Online Centroid Anomaly Detection
2010/3/11
Security issues are crucial in a number of machine learning applications, especially in
scenarios dealing with human activity rather than natural phenomena (e.g., information
ranking, spam detection...