Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  benchmark several SVM objective functions for large-scale image classification.  consider one-versus-rest, multiclass, ranking, and ighted approximate ranking SVMs. A comparison of online and batch methods for optimizing the objectives shows that online methods perform as ll as batch methods in terms of classification accuracy, but with a significant gain in training speed. Using stochastic gradient descent,  can scale the training to millions of images and thousands of classes. Our experimental evaluation shows that ranking-based algorithms do not outperform the one-versus-rest strategy when a large number of training examples are used. Furthermore, the gap in accuracy beten the different algorithms shrinks as the dimension of the features increases.  also show that learning through cross-validation the optimal rebalancing of positive and negative examples can result in a significant improvement for the one-versus-rest strategy. Finally, early stopping can be used as an effective regularization strategy when training with online algorithms. Following these "good practices,"  re able to improve the state of the art on a large subset of 10K classes and 9M images of ImageNet from 16.7 percent Top-1 accuracy to 19.1 percent.
['Zeynep Akata', 'Florent Perronnin', 'Zaid Harchaoui', 'Cordelia Schmid']
I am writing a research paper on a idea Good Practice in Large-Scale Learning for Image Classification
[{"from": "human", "value": "I am writing a research paper on a idea Good Practice in Large-Scale Learning for Image Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  benchmark several SVM objective functions for large-scale image classification.  consider one-versus-rest, multiclass, ranking, and ighted approximate ranking SVMs. A comparison of online and batch methods for optimizing the objectives shows that online methods perform as ll as batch methods in terms of classification accuracy, but with a significant gain in training speed. Using stochastic gradient descent,  can scale the training to millions of images and thousands of classes. Our experimental evaluation shows that ranking-based algorithms do not outperform the one-versus-rest strategy when a large number of training examples are used. Furthermore, the gap in accuracy beten the different algorithms shrinks as the dimension of the features increases.  also show that learning through cross-validation the optimal rebalancing of positive and negative examples can result in a significant improvement for the one-versus-rest strategy. Finally, early stopping can be used as an effective regularization strategy when training with online algorithms. Following these \"good practices,\"  re able to improve the state of the art on a large subset of 10K classes and 9M images of ImageNet from 16.7 percent Top-1 accuracy to 19.1 percent."}]