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International Conference on machine Learning and Cybernetics

作者： Zhang, Jian Zhao, Hai Lu, Bao-Liang Department of Computer Science and Engineering Center for Brain-Like Computing and Machine Intelligence Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai 200240 China

ISBN: (纸本)9781424465262

Patent classification is a large scale hierarchical text classification (LSHTC) task. Though comprehensive comparisons, either learning algorithms or feature selection strategies, have been fully made in the text categorization field, few work was done for a LSHTC task due to high computational cost and complicated structural label characteristics. For the first time, this paper compares two popular learning frameworks, namely hierarchical support vector machine (SVM) and k nearest neighbor (k-NN) that are applied to a LSHTC task. Experiment results show that the latter outperforms the former in this LSHTC task, which is quite different from the usual results for normal text categorization tasks. Then this paper does a comparative study on different similarity measures and ranking approaches in k-NN framework for LSHTC task. Conclusions can be drawn that k-NN is more appropriate for the LSHTC task than hierarchical SVM and for a specific LSHTC task. BM25 outperforms other similarity measures and List Weak gains a better performance than other ranking approaches. We also find an interesting phenomenon that using all the labels of the retrieved neighbors can remarkably improve classification performance over only using the first label of the retrieved neighbors. © 2010 IEEE.

关键词： Learning algorithms

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Enhance Top-down method with Meta-Classification for Very Large-scale Hierarchical Classification 5

Enhance Top-down method with Meta-Classification for Very La...

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5th International Joint Conference on Natural Language Processing, IJCNLP 2011

作者： Wang, Xiao-Lin Zhao, Hai Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China

ISBN: (纸本)9789744665645

Recent large-scale hierarchical classification tasks typically have tens of thousands of classes as well as a large number of samples, for which the dominant solution is the top-down method due to computational complexity. However, the top-down method suffers from accuracy deficiency, that is, its accuracy is generally lower than that of the flat approach of 1-vs-Rest. In this paper, we employ meta-classification technique to enhance the classifying procedure of the top-down method. We analyze the proposed method on the aspect of accuracy, and then test it with two real-world large-scale data sets. Our method both maintains the efficiency of the conventional top-down method and provides competitive classification accuracies. © 2011 AFNLP

关键词： Natural language processing systems

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ZeroED: Hybrid Zero-shot Error Detection through Large Language Model Reasoning

arXiv

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arXiv 2025年

作者： Ni, Wei Zhang, Kaihang Miao, Xiaoye Zhao, Xiangyu Wu, Yangyang Wang, Yaoshu Yin, Jianwei Center for Data Science Zhejiang University Hangzhou China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Data Science City University of Hong Kong Hong Kong Software College Zhejiang University Ningbo China Shenzhen Institute of Computing Sciences Shenzhen China College of Computer Science Zhejiang University Hangzhou China

Error detection (ED) in tabular data is crucial yet challenging due to diverse error types and the need for contextual understanding. Traditional ED methods often rely heavily on manual criteria and labels, making them labor-intensive. Large language models (LLM) can minimize human effort but struggle with errors requiring a comprehensive understanding of data context. In this paper, we propose ZeroED, a novel hybrid error detection framework, which combines LLM reasoning ability with the machine learning pipeline via zero-shot prompting. ZeroED operates in four steps, i.e., feature representation, error labeling, training data construction, and detector training. Initially, to enhance error distinction, ZeroED generates rich data representations using LLM-driven error reason-aware binary features, pre-trained embeddings, and statistical features. Then, ZeroED employs LLM to holistically label errors through in-context learning, guided by a two-step LLM reasoning process for detailed ED guidelines. To reduce token costs, LLMs are applied only to representative data selected via clustering-based sampling. High-quality training data is constructed through in-cluster label propagation and LLM augmentation with verification. Finally, a classifier is trained to detect all errors. Extensive experiments on seven datasets demonstrate that, ZeroED outperforms state-of-the-art methods by a maximum 30% improvement in F1 score and up to 90% token cost reduction. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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An Empirical Study on Development Set Selection Strategy for machine Translation Learning∗ 5

An Empirical Study on Development Set Selection Strategy for...

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Joint 5th Workshop on Statistical machine Translation and MetricsMATR, WMT 2010 at the 48th Conference of the Associationfor Computational Linguistics, ACL 2010

作者： Hui, Cong Zhao, Hai Song, Yan Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University China MOE-Microsoft Key Laboratory for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dong Chuan Rd. Shanghai 200240 China Department of Chinese Translation and Linguistics City University of Hong Kong Hong Kong

ISBN: (纸本)9781932432718

This paper describes a statistical machine translation system for our participation for the WMT10 shared task. Based on MOSES, our system is capable of translating German, French and Spanish into English. Our main contribution in this work is about effective parameter tuning. We discover that there is a significant performance gap as different development sets are adopted. Finally, ten groups of development sets are used to optimize the model weights, and this does help us obtain a stable evaluation result. © 2010 Association for Computational Linguistics

关键词： machine translation

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Measuring Sleep Quality from EEG with machine Learning Approaches

Measuring Sleep Quality from EEG with Machine Learning Appro...

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International Joint Conference on Neural Networks

作者： Li-Li Wang Wei-Long Zheng Hai-Wei Ma Bao-Liang Lu Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center

ISBN: (纸本)9781509006212

This study aims at measuring last-night sleep quality from electroencephalography (EEG). We design a sleep experiment to collect waking EEG signals from eight subjects under three different sleep conditions: 8 hours sleep, 6 hours sleep, and 4 hours sleep. We utilize three machine learning approaches, k-Nearest Neighbor (kNN), support vector machine (SVM), and discriminative graph regularized extreme learning machine (GELM), to classify extracted EEG features of power spectral density (PSD). The accuracies of these three classifiers without feature selection are 36.68%, 48.28%, 62.16%, respectively. By using minimal-redundancy-maximal-relevance (MRMR) algorithm and the brain topography, the classification accuracy of GELM with 9 features is improved largely and increased to 83.57% in average. To investigate critical frequency bands for measuring sleep quality, we examine the features of each band and observe their energy changing. The experimental results indicate that Gamma band is more relevant to measuring sleep quality.

关键词： quality of sleep Electroencephalography Sleep Artificial intelligence machine Learning Frequency bands Power spectral density Feature extraction

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Driving Fatigue Detection with Fusion of EEG and Forehead EOG

Driving Fatigue Detection with Fusion of EEG and Forehead EO...

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International Joint Conference on Neural Networks

作者： Xue-Qin Huo Wei-Long Zheng Bao-Liang Lu Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center

ISBN: (纸本)9781509006212

In this paper, we fuse EEG and forehead EOG to detect drivers' fatigue level by using discriminative graph regularized extreme learning machine (GELM). Twenty-one healthy subjects including twelve men and nine women participate in our driving simulation experiments. Two fusion strategies are adopted: feature level fusion (FLF) and decision level fusion (DLF). PERCLOS (the percentage of eye closure) is calculated by using the eye movement data recorded by eye tracking glasses as the indicator of drivers' fatigue level. The prediction correlation coefficient and root mean square error (RMSE) between the estimated fatigue level and the real fatigue level are both used to evaluate the performance of single modality and fusion modality. A comparative study on modality performance is conducted between GELM and support vector machine (SVM). The experimental results show that fusion modality can improve the performance of driving fatigue detection with a higher prediction correlation coefficient and a lower RMSE value in comparison with solely using EEG or forehead EOG. And FLF achieves better performance than DLF. GELM is more suitable for driving fatigue detection than SVM. Moreover, feature level fusion with GELM achieves the best performance with the prediction correlation coefficient of 0.8080 and the RMSE value of 0.0712 on average.

关键词： Fatigue Electrooculography Feature extraction Electroencephalography Forehead brain modeling Correlation

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Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine

Automatic artifact removal from EEG - a mixed approach based...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Georg Bartels Li-Chen Shi Bao-Liang Lu Shanghai Jiao Tong University Shanghai CN Center of Brain Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiaotong University China

Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.

关键词： Electroencephalography Electrooculography Electromyography Support vector machines Algorithm design and analysis brain modeling Muscles

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Marginalized denoising autoencoder via graph regularization for domain adaptation

Marginalized denoising autoencoder via graph regularization ...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MoE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Domain adaptation, which aims to learn domain-invariant features for sentiment classification, has received increasing attention. The underlying rationality of domain adaptation is that the involved domains share some common latent factors. Recently neural network based on Stacked Denoising Auto-Encoders (SDA) and its marginalized version (mSDA) have shown promising results on learning domain-invariant features. To explicitly preserve the intrinsic structure of data, this paper proposes a marginalized Denoising Autoencoders via graph Regularization (GmSDA) in which the autoencoder based framework can learn more robust features with the help of newly incorporated graph regularization. The learned representations are fed into the sentiment classifiers and experiments show that the GmSDA can effectively improve the classification accuracy when comparing with some state-of-the-art models on the cropped Amazon benchmark data set. © Springer-Verlag 2013.

关键词： Classification (of information)

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Structure preserving low-rank representation for semi-supervised face recognition

Structure preserving low-rank representation for semi-superv...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Suhang Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has shown excellent performance in semi-supervised learning. In this paper, we additionally impose twofold constraints (local affinity and distant repulsion) on the LRR graph. The improved model, termed structure preserving LRR (SPLRR), can preserve the local geometrical structure but without distorting the distant repulsion property. Experiments are taken on three widely used face data sets to investigate the performance of SPLRR and the results show that it is superior to some state-of-the-art semi-supervised graphs. © Springer-Verlag 2013.

关键词： Face recognition

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective computing 2024年

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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