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Found 12 papers, 0 papers with code
Your 2 is My 1, Your 3 is My 9: Handling Arbitrary Miscalibrations in Ratings
A popular approach to address this issue is to assume simplistic models of miscalibration (such as linear biases) to de-bias the scores.
Cross-domain attribute representation based on convolutional neural network
We proposed to embed the attributes of dif-ferent domains by a shared convolutional neural network (CNN), learn a domain-independent CNN model to represent the information shared by dif-ferent domains by matching across domains, and a domain-specific CNN model to represent the information of each domain.
Domain transfer convolutional attribute embedding
In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain.
A novel image tag completion method based on convolutional neural network
In this paper, we study the problem of image tag complete and proposed a novel method for this problem based on a popular image representation method, convolutional neural network (CNN).
Learning convolutional neural network to maximize Pos@Top performance measure
The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation.
Image tag completion by local learning
The problem of tag completion is to learn the missing tags of an image.
Representing data by sparse combination of contextual data points for classification
In this objective, the reconstruction error is minimized and the coefficient spar- sity is encouraged.
Joint-ViVo: Selecting and Weighting Visual Words Jointly for Bag-of-Features based Tissue Classification in Medical Images
Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc.
Adaptive Graph via Multiple Kernel Learning for Nonnegative Matrix Factorization
In this paper, we propose a novel idea which engages a Multiple Kernel Learning approach into refining the graph structure that reflects the factorization of the matrix and the new data space.
Discriminative Sparse Coding on Multi-Manifold for Data Representation and Classification
Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditional codebooks and codes to maximize the manifold margins of different classes.
Low-Rank Deep Convolutional Neural Network for Multi-Task Learning
Meanwhile, we also propose to regularize another fully connected layer by sparsity penalty, so that the useful features learned by the lower layers can be selected.
Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons
We show that the MLE incurs a suboptimal rate in terms of bias.
