Provided in the present invention are a method and apparatus for labeling training samples. In the embodiments of the present invention, two mutually independent classifiers, i.e. a first classifier and a second classifier, are used to perform collaborative forecasting on M unlabeled first training samples to obtain some of the labeled first training samples, without the need for the participation of operators; the operation is simple and the accuracy is high, thereby improving the efficiency and reliability of labeling training samples.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A method for labeling training samples, comprising: inputting M unlabeled first training samples into a first classifier to obtain a first forecasting result of each first training sample in the M first training samples, M being an integer greater than or equal to 1; selecting N first training samples as second training samples from the M first training samples based upon the first forecasting result of each first training sample, N being an integer greater than or equal to 1 and less than or equal to M; inputting the N second training samples into a second classifier to obtain a second forecasting result of each second training sample in the N second training samples, the first classifier and the second classifier being independent of each other; selecting P second training samples from the N second training samples based upon the second forecasting result of each second training sample, P being an integer greater than or equal to 1 and less than or equal to N; selecting Q first training samples from other first training samples based upon the first forecasting results of other first training samples in the M first training samples apart from the N second training samples and the value of P, Q being an integer greater than or equal to 1 and less than or equal to a difference between M and N; and generating P labeled second training samples based upon second forecasting results of the P second training samples and each of the second training samples therein; and generating Q labeled first training samples based upon first forecasting results of the Q first training samples and each of the first training samples therein. 22. The method of claim 21, wherein, said selecting the N first training samples comprises: obtaining a first probability that said first training samples indicated by the first forecasting result are of a preselected type; and selecting, from the M first training samples, the N first training samples of which the first probability satisfies a pre-set first training condition as the second training samples. 23. The method of claim 22, wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of a designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value. 24. The method of claim 22, wherein said selecting the P second training samples comprises: obtaining a second probability that the second training samples indicated by the second forecasting result are of a designated type; and selecting, from the N second training samples, the P second training samples of which the second probability satisfies a pre-set second training condition. 25. The method of claim 24, wherein the second training condition comprises a designated number with a minimum probability that the second training samples indicated by the second forecasting result are of a designated type. 26. The method of claim 24, wherein said selecting the Q first training samples comprises: selecting, from the other first training samples, P first training samples of which a third probability that the first training samples indicated by the first forecasting result are of a designated type satisfies a pre-set third training condition; and selecting, from the other first training samples, Q−P first training samples of which the third probability satisfies a pre-set fourth training condition. 27. The method of claim 26, wherein the third training condition comprises a designated number with a minimum probability that the first training samples indicated by the first forecasting result are of a designated type. 28. The method of claim 26, wherein the fourth training condition comprises a designated number with the maximum probability that the first training samples indicated by the first forecasting result are of a designated type. 29. The method of claim 26, wherein a ratio of Q−P to 2P is a golden ratio. 30. The method of claim 22, wherein the designated type comprises at least one of a positive-example type and a counter-example type. 31. An apparatus for labeling training samples, comprising: a classification system for inputting M unlabeled first training samples into a first classifier to obtain a first forecasting result of each first training sample in the M first training samples, M being an integer greater than or equal to 1; a selection system for, according to the first forecasting result of each first training sample, selecting, from the M first training samples, N first training samples as second training samples, N being an integer greater than or equal to 1 and less than or equal to M; said classification system further being for inputting the N second training samples into a second classifier to obtain a second forecasting result of each second training sample in the N second training samples, the first classifier and the second classifier being independent of each other; said selection system further being for, according to the second forecasting result of each second training sample, selecting, from said N second training samples, P second training samples, P being an integer greater than or equal to 1 and less than or equal to N; said selection system further being for, according to first forecasting results of other first training samples in the M first training samples, apart from the N second training samples, and the value of P, selecting, from the other first training samples, Q first training samples, Q being an integer greater than or equal to 1 and less than or equal to a difference of M−N; and a processing system for generating, according to second forecasting results of the P second training samples and each of the second training samples, P labeled second training samples, and generating, according to first forecasting results of the Q first training samples and each of the first training samples therein, Q labeled first training samples. 32. The apparatus of claim 31, wherein said said selection system is configured for: obtaining a first probability that said first training samples indicated by the first forecasting result are of a preselected type; and selecting, from the M first training samples, the N first training samples of which the first probability satisfies a pre-set first training condition as the second training samples. 33. The apparatus of claim 32, wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of a designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value. 34. The apparatus of claim 32, wherein said selection system is configured for: obtaining a second probability that the second training samples indicated by the second forecasting result are of a designated type; and selecting, from the N second training samples, the P second training samples of which the second probability satisfies a pre-set second training condition. 35. The apparatus of claim 34, wherein the second training condition comprises a designated number with a minimum probability that the second training samples indicated by the second forecasting result are of a designated type. 36. The apparatus of claim 34, wherein said selection system is configured for: selecting, from the other first training samples, P first training samples of which a third probability that the first training samples indicated by the first forecasting result are of a designated type satisfies a pre-set third training condition; and selecting, from the other first training samples, Q−P first training samples of which the third probability satisfies a pre-set fourth training condition. 37. The apparatus of claim 36, wherein the third training condition comprises a designated number with a minimum probability that the first training samples indicated by the first forecasting result are of a designated type. 38. The apparatus of claim 36, wherein the fourth training condition comprises a designated number with a maximum probability that the first training samples indicated by the first forecasting result are of a designated type. 39. The apparatus of claim 36, wherein the ratio of Q−P to 2P is a golden ratio. 40. The apparatus of claim 39, wherein the designated type comprises at least one of a positive-example type and a counter-example type.