Unsupervised Domain Adaptation Using Exemplar‐SVMs with Adaptation Regularization

Abstract

Domain adaptation has recently attracted attention for visual recognition. It assumes that source and target domain data are drawn from the same feature space but different margin distributions and its motivation is to utilize the source domain instances to assist in training a robust classifier for target domain tasks. Previous studies always focus on reducing the distribution mismatch across domains. However, in many real-world applications, there also exist problems of sample selection bias among instances in a domain; this would reduce the generalization performance of learners. To address this issue, we propose a novel model named Domain Adaptation Exemplar Support Vector Machines (DAESVMs) based on exemplar support vector machines (exemplar-SVMs). Our approach aims to address the problems of sample selection bias and domain adaptation simultaneously. Comparing with usual domain adaptation problems, we go a step further in slacking the assumption of i.i.d. First, we formulate the DAESVMs training classifiers with reducing Maximum Mean Discrepancy (MMD) among domains by mapping data into a latent space and preserving properties of original data, and then, we integrate classifiers to make a prediction for target domain instances. Our experiments were conducted on Office and Caltech10 datasets and verify the effectiveness of the model we proposed.

A Comparative Study of VMD-Based Hybrid Forecasting Model for Nonstationary Daily Streamflow Time Series

“…Numerous classical black box time series models, which include the autoregression (AR) model, the autoregressive moving average (ARMA) model, and the autoregressive integrated moving average (ARIMA) model [4,5], have been used in streamflow forecasting since 1970. ese models are linear and therefore miss the nonlinear and nonstationary characteristics that are hidden in the real streamflow series. Hence, researchers have focused on developing strong nonlinear mapping abilities (machine learning techniques) to overcome these drawbacks, including decision trees such as the gradient boosted regression tree (GBRT) [6,7], the kernel methods such as the support vector machine (SVM) [8,9], and the support vector regression (SVR) [10]. e SVM [11,12] and the SVR [13,14] have been used in the field of streamflow forecasting research.…”