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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 |

e or not). Setup Let us formalize the supervised 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. <span>The entire training data is denoted as 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

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 |

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

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 |