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. An apparatus for labeling training samples, comprising: one or more processors; and a memory having one or more programs stored thereon to be executed by said one or more processors, the programs including instruction 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; selecting N first training samples as second training samples from the M first training samples according to 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 said N second training samples according to 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 the other first training samples 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, Q being an integer greater than or equal to 1 and less than or equal to a difference of M−N; and generating P labeled second training samples according to second forecasting results of the P second training samples and each of the second training samples; and generating Q labeled first training samples according to first forecasting results of the Q first training samples and each of the first training samples therein. 2. The apparatus of claim 1, wherein the programs include instruction for: obtaining a first probability that said first training samples indicated by the first forecasting result are of a designated 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; or wherein the programs include instruction for: obtaining a second probability that the second training samples indicated by the second forecasting result are of the 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. 3. The apparatus of claim 2, wherein the designated type comprises a positive-example type, a counter-example type, or a combination thereof. 4. The apparatus of claim 2, wherein the first training condition comprises a probability that the first training samples indicated by the first forecasting result are of the designated type is greater than or equal to a first threshold value and is less than or equal to a second threshold value; or 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 the designated type. 5. The apparatus of claim 1, wherein the programs include instruction 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. 6. The apparatus of claim 5, 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; or 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. 7. The apparatus of claim 1, wherein a ratio of Q−P to 2P is a golden ratio. 8. A method for labeling training samples, comprising: selecting P second training samples from N second training samples selected from M first training samples based upon a first forecasting result of each of the M first training samples, the P second training samples being selected based upon a second forecasting result of each of the N second training samples; 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; 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. 9. The method of claim 8, wherein M is an integer greater than or equal to 1, wherein N is an integer between 1 and M inclusive, wherein P is an integer between 1 and N inclusive, and wherein Q is an integer between 1 and a difference between M and N inclusive. 10. The method of claim 8, further comprising inputting the M first training samples into a first classifier to obtain the first forecasting result of the each of the M first training samples. 11. The method of claim 10, wherein said inputting the M first training samples comprises inputting the M first training samples as unlabeled first training samples. 12. The method of claim 8, further comprising selecting N first training samples as the N second training samples from the M first training samples based upon the first forecasting result of the each of the M first training samples. 13. The method of claim 8, further comprising inputting the N second training samples into a second classifier to obtain the second forecasting result of the each of the N second training samples. 14. The method of claim 13, wherein the second classifier is independent from a first classifier used for obtaining the first forecasting result of the each of the M first training samples. 15. An apparatus for labeling training samples, comprising: one or more processors; and a memory having one or more programs stored thereon to be executed by said one or more processors, the programs including instruction for a labeling process including: instruction for selecting P second training samples from N second training samples selected from M first training samples based upon a first forecasting result of each of the M first training samples, the P second training samples being selected based upon a second forecasting result of each of the N second training samples; instruction for 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; instruction for 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 instruction for 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. 16. The apparatus of claim 15, wherein the instruction for the labeling process includes instruction for inputting the M first training samples into a first classifier to obtain the first forecasting result of the each of the M first training samples, wherein the M first training samples are unlabeled. 17. The apparatus of claim 16, wherein the first classifier is based on a training sample set, wherein the instruction for the labeling process includes instruction for adding the P labeled second training samples and the Q labeled first training samples to the training sample set, and wherein the programs include instruction for repeatedly executing the instruction for the labeling process until a classification accuracy rate based on the first classifier is greater than or equal to a pre-set accuracy rate threshold value. 18. The apparatus of claim 16, wherein the first classifier is based on a training sample set, wherein the instruction for the labeling process includes instruction for adding the P labeled second training samples and the Q labeled first training samples to the training sample set, and wherein the programs include instruction for repeatedly executing the instruction for the labeling process until a number of the first training samples contained in the training sample set is greater than or equal to a pre-set number threshold value. 19. The apparatus of claim 15, wherein the instruction for the labeling process includes instruction for selecting N first training samples as the N second training samples from the M first training samples based upon the first forecasting result of the each of the M first training samples. 20. The apparatus of claim 15, wherein the instruction for the labeling process includes instruction for inputting the N second training samples into a second classifier to obtain the second forecasting result of the each of the N second training samples, wherein the second classifier is independent from a first classifier used for obtaining the first forecasting result of the each of the M first training samples.