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Learning to Predict Bus Arrival Time From Heterogeneous Measurements via Recurrent Neural Network.

, , , , , and . IEEE Trans. Intell. Transp. Syst., 20 (9): 3283-3293 (2019)

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Undoing the codebook bias by linear transformation with sparsity and F-norm constraints for image classification., , , , , , and . Pattern Recognit. Lett., (2014)Pedestrian detection via logistic multiple instance boosting., , , and . ICIP, page 1464-1467. IEEE, (2008)Image classification using boosted local features with random orientation and location selection., , , , , , , and . Inf. Sci., (2015)Interactive event detection in crowd scenes., , , and . ICIMCS, page 46-49. ACM, (2012)Online dictionary learning for Local Coordinate Coding with Locality Coding Adaptors., , , , , , and . Neurocomputing, (2015)A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification., , , and . CoRR, (2024)Undo the codebook bias by linear transformation for visual applications., , , , , , and . ACM Multimedia, page 533-536. ACM, (2013)Beyond Explicit Codebook Generation: Visual Representation Using Implicitly Transferred Codebooks., , , , , and . IEEE Trans. Image Processing, 24 (12): 5777-5788 (2015)Local Laplacian Coding From Theoretical Analysis of Local Coding Schemes for Locally Linear Classification., , , , , and . IEEE Trans. Cybern., 45 (12): 2937-2947 (2015)Topic detection in cross-media: a semi-supervised co-clustering approach., , , , and . Int. J. Multim. Inf. Retr., 3 (3): 193-205 (2014)