D = { ( x 1 , y 1 ) , … , ( x n , y n ) } ⊆ R d × C
Question
[default - edit me]
Answer
?
Question
[default - edit me]
Answer
D = { ( x 1 , y 1 ) , … , ( x n , y n ) } ⊆ R d × C
If you want to change selection, open document below and click on "Move attachment"
Lecture 1: Supervised Learning upervised machine learning setup. Our training data comes in pairs of inputs (x,y) ( x , y ) , where x∈Rd x ∈ R d is the input instance and y y its label. The entire training data is denoted as <span>D={(x1,y1),…,(xn,yn)}⊆Rd×C D = { ( x 1 , y 1 ) , … , ( x n , y n ) } ⊆ R d × C where: Rd R d is the d-dimensional feature space xi x i is the input vector of the ith i t h sample yi y i is the label of the ith i
Summary
status
not learned
measured difficulty
37% [default]
last interval [days]
repetition number in this series
0
memorised on
scheduled repetition
scheduled repetition interval
last repetition or drill
Details
No repetitions
Discussion
Do you want to join discussion? Click here to log in or create user.