A classifier training method includes detecting error data from training data; and training a classifier configured to detect an object based on the error data.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A classifier training method, comprising: detecting error data from training data; and training a classifier configured to detect an object based on the error data. 2. The method of claim 1, wherein, the error data includes at least one of first error data and second error data, the first error data is data, from among the training data, for which classification is not completed, and the second error data is data, from among the training data, that includes an object which is misclassified as another object. 3. The method of claim 1, wherein the detecting comprises: detecting the error data by classifying the training data using a first classifier trained based on the training data. 4. The method of claim 1, wherein the detecting comprises: training a first classifier based on the training data; detecting first error data by classifying the training data using the first classifier; training a second classifier based on the first error data; and detecting second error data by classifying the training data using the second classifier. 5. The method of claim 4, further comprising: training a third classifier based on the second error data. 6. The method of claim 4, wherein, the second error data includes, data, from among the training data, that is correctly classified by the first classifier and is not correctly classified by the second classifier, and data, from among the training data, that is not correctly classified by the first classifier and is not correctly classified by the second classifier. 7. The method of claim 1, further comprising: verifying the trained classifier. 8. The method of claim 7, wherein the verifying comprises: adjusting a parameter of the trained classifier such that a false acceptance rate (FAR) corresponding to the trained classifier is less than or equal to a threshold value. 9. The method of claim 1, further comprising: setting a first ratio, wherein the training data includes positive data and negative data such that a ratio of the positive data to the negative data is the first ratio. 10. An object detecting method, comprising: receiving input data; and detecting a target object from the input data using a classifier trained based on error data. 11. The method of claim 10, further comprising: detecting the error data by training a first classifier using training data and classifying the training data using the first classifier. 12. The method of claim 10, further comprising: detecting the error data by, training a first classifier using training data, classifying the training data using the first classifier to detect first error data, training a second classifier using the first error data, and classifying the training data using the second classifier. 13. The method of claim 10, further comprising: training a first classifier using training data; detecting the error data by classifying the training data using the first classifier; and training a second classifier using the error data. 14. The method of claim 10, further comprising: training a first classifier using training data; detecting first error data by classifying the training data using the first classifier; training a second classifier using the first error data; detecting the error data by classifying the training data using the second classifier; and training a third classifier using the error data. 15. A classifier training apparatus, comprising: a memory configured to store training data; and a trainer configured to detect error data from the training data and train a classifier such that the classifier is configured to detect an object based on the error data. 16. The apparatus of claim 15, wherein the trainer is configured to detect the error data by classifying the training data using a first classifier trained based on the training data. 17. The apparatus of claim 15, wherein the trainer is configured to, detect first error data by, training a first classifier based on the training data, and classifying the training data using the first classifier, and detect second error data by, training a second classifier based on the first error data, and classifying the training data using the second classifier. 18. The apparatus of claim 17, wherein the trainer is configured to train a third classifier based on the second error data. 19. The apparatus of claim 15, wherein the trainer is configured to adjust a parameter of the classifier such that a false acceptance rate (FAR) corresponding to the trained classifier is less than or equal to a preset threshold value. 20. The apparatus of claim 15, wherein the training data comprises positive data and negative data at a preset ratio.