### Table 1: Sample complexity for learning with squared loss.

1998

"... In PAGE 2: ... An agnostic learning algorithm can also be used to learn the best approximation to the target function when the target function is not in the class. Table1 shows some of the known results for learning with squared loss. (The technical conditions such as pseudo-dimension and covering number are described in Section 2.... ..."

Cited by 31

### Table 1: Sample complexity for learning with squared loss.

1998

"... In PAGE 1: ... An agnostic learning algorithm can also be used to learn the best approximation to the target function when the target function is not in the class. Table1 shows some of the known results for learning with squared loss. (The technical conditions such as pseudo-dimension and covering number are described in Section 2).... ..."

Cited by 31

### Table 1: Sample complexity for learning with squared loss.

1997

"... In PAGE 2: ... An agnostic learning algorithm can also be used to learn the best approximation to the target function when the target function is not in the class. Table1 shows some of the known results for learning with squared loss. (The technical conditions such as pseudo-dimension and covering number are described in Section 2.... ..."

### Table 4. Comparison of sample complexity bounds. Algorithm Lower bound on sample complexity N

2002

Cited by 7

### Table 4. Comparison of sample complexity bounds. Algorithm Lower bound on sample complexity N

2002

Cited by 7

### Table 1. Complexity of sample applications

2006

"... In PAGE 10: ... Finally, AcousticLocalization is able to determine the distance of neighboring sensor nodes by taking advantage of the difference in speed of radio waves and sound. Table1 gives details about the complexity of the three applications showing the respective code size, the number of nesC components and the number of binary components. Table 1.... ..."

Cited by 17

### TABLE I COMPLEXITY OF SAMPLE APPLICATIONS

2006

Cited by 3