阅读理解 Classifying things is critical for our daily lives. For example, we have to detect spam mail (垃

阅读理解
Classifying things is critical for our daily lives. For example, we have to detect spam mail (垃圾邮件), false political news. When we use AI, such tasks are based on “classification technology” in machine learning—having the computer learn, using the boundary separating positive and negative data. For example, “positive” data would be photos including a happy face, and “negative” data photos that include a sad face. Once a classification boundary is learned, the computer can determine whether a certain data is positive or negative.
However, the difficulty with this technology is that it requires both positive and negative data for the learning process, and negative data are not available in many cases. For instance, when a retailer (零售商) is trying to predict who will make a purchase, they can easily find data on customers who have purchased from them (positive data), but it is basically impossible to obtain data on customers who have never purchased from them (negative data), since they do not have access to their competitors’ data.
According to lead author Takashi Ishida from RIKEN AIP, “Previous classification methods could not cope with the situation where negative data were not available, but we have made it possible for computers to learn with only positive data, as long as we have a confidence score for our positive data, constructed from information such as buying intention or the active rate of app users. Using our new method, we can let computers learn a classifier only from positive data equipped with confidence.”
According to Ishida, “This discovery could expand the range of applications where classification technology can be used. Even in fields where machine learning has been actively used, our classification technology could be used in new situations where only positive data can be gathered due to data regulation or business constraints (限制). In the near future, we hope to put our technology to use in various research fields, such as natural language processing, computer vision, robotics, and bioinformatics.”
7. How can the computer distinguish the positive data from the negative data?
A. By learning the classification boundary.
B. By updating the data collected regularly.
C. By separating happy faces and sad ones.
D. By introducing classification technology.
8. Why is the example mentioned in Paragraph 2?
A. To prove how important the positive data are.
B. To confirm that data on customers are complete.
C. To argue that retailers get their competitors’ data.
D. To explain why negative data are hard to acquire.
9. What do the underlined words “new method” in Paragraph 3 refer to?
A. Analyzing buying intention.
B. Building a confidence score.
C. Assessing the active rate of app users.
D. Equipping the computer with confidence.
10. What can be a suitable title for the text?
A. The History of Classification Technology
B. Smarter AI: Machine Leaning without Negative Data
C. Bigger Data: Computers Assisting Language Processing
D. The Comparison between Positive Data and Negative Data
【答案】7. A    8. D    9. B    10. B
【解析】