Transfer learning addresses the problem of how to uti- lize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the training and testing problems have different distributions or features. Experimental evaluation on different kinds of datasets shows that our proposed algorithm can improve the performance of cross-domain text clas- sification significantly. The main contribution of this study is to merge these two approaches to transfer only the relevant knowledge in a setting. Currently, there is a need for an objective measure to assess intubation difficulties in emergency situations by physicians, residents, and paramedics who are unfamiliar with tracheal intubation. This paper focuses on a new clustering task, called self-taught clustering. 0000015889 00000 n An overall 91% mean average precision for coconut trees detection was achieved. In our initial application of the technique, we used a dataset consisting of 266 earthquakes recorded by 39 stations. Assum- ing Dp and Da are two sets of examples drawn from two mismatched distributions, where Da are fully labeled and Dp partially labeled, our objective is to complete the la- bels of Dp. However, new skills acquisition through RL usually starts from a state of tabula rasa, which is a time-exhausted procedure and can not be suitable for real physical robot, especially when task is a complicated one. As the World Wide Web in China grows rapidly, mining knowledge in Chinese Web pages becomes more and more important. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., T ransfer learning, F ederated learning, and E volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. 0000143311 00000 n In this paper, we propose a novel cross-domain text classification algorithm which extends the traditional probabilis- tic latent semantic analysis (PLSA) algorithm to integrate labeled and unlabeled data, which come from different but related do- mains, into a unified probabilistic model. 0000004984 00000 n People can often transfer knowledge learnt previously to novel situations Chess Checkers Mathematics Computer Science Table Tennis Tennis . Labeling the new data can be costly and it would also be a waste to throw away all the old data. To solve this problem, we learn a low-dimensional latent feature space where the distributions between the source do- main data and the target domain data are the same or close to each other. This paper is currently under review Authors: Sebastian Monka. 0000007556 00000 n We present the SR2LR algorithm that finds an effective mapping of the source model to the target domain in this set-ting and demonstsrate its effectiveness in three relational do-mains. We propose a framework for solving this problem, which is based on reg- ularization with spectral functions of matrices. 0000004823 00000 n Speech recognition plays an important role in digital transformation. Experimental datasets from both HT-PEMFCs and HT-PEM ECHPs with different materials and operating conditions (~50 points each) were used to train 8 target models via FSL. We show that transfer learning of feature relevance improves performance on two real data sets which illustrate such settings: (1) predicting ratings in a collaborative filtering task, and (2) distinguishing arguments of a verb in a sentence. We will introduce the most significant and relevant techniques that may provide some directions in the future research. Responsible editor: Guest Editors DeepL4KGs 2021. We evaluate SDCA on multiple benchmarks, achieving considerable improvements over existing algorithms.The code is publicly available at https://github.com/BIT-DA/SDCA. It often happens that obtaining labeled data in a new domain is expensive and time consuming, while there may be plenty of labeled data from a related but different domain.