Implement (in C++) the FIND-S algorithm (chapter 2 [Mitchell, 1997]). Use the training examples in table 1 to verify that it successfully produces the trace described in section 2.4 [Mitchell, 1997] for the Enjoysport example. Q1: Now use this program to determine the number of additional training examples required to exactly learn the target concept:
< Sunny, W arm, ?, ?, ?, ? >
In a ZIP file, place the source code, executable, and a text file containing your list of random training examples and the answer to Q1. Upload ZIP file to Vula before 10.00 AM, 19 August.
Table 1: Positive and negative training examples for target concept EnjoySport
| Example | Sky | AirTemp | Humidity | Wind | Water | Forecast | EnjoySport |
| 1 | Sunny | Warm | Normal | Strong | Warm | Same | Yes |
| 2 | Sunny | Warm | High | Strong | Warm | Same | Yes |
| 3 | Rainy | Cold | High | Strong | Warm | Change | No |
| 4 | Sunny | Warm | High | Strong | Cool | Change | Yes |
1
References
[Mitchell, 1997] Mitchell, T. (1997). Machine Learning. McGraw Hill, New York, USA.
