A Characterization of Online Multiclass Learnability

03/30/2023
by   Vinod Raman, et al.
0

We consider the problem of online multiclass learning when the number of labels is unbounded. We show that the Multiclass Littlestone dimension, first introduced in <cit.>, continues to characterize online learnability in this setting. Our result complements the recent work by <cit.> who give a characterization of batch multiclass learnability when the label space is unbounded.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/08/2023

Multiclass Online Learnability under Bandit Feedback

We study online multiclass classification under bandit feedback. We exte...
research
01/06/2023

A Characterization of Multilabel Learnability

We consider the problem of multilabel classification and investigate lea...
research
07/30/2022

A point to set principle for finite-state dimension

Effective dimension has proven very useful in geometric measure theory t...
research
01/28/2023

An Unbounded Fully Homomorphic Encryption Scheme Based on Ideal Lattices and Chinese Remainder Theorem

We propose an unbounded fully homomorphic encryption scheme, i.e. a sche...
research
11/30/2021

One-step replica symmetry breaking of random regular NAE-SAT II

Continuing our earlier work in <cit.>, we study the random regular k-NAE...
research
07/07/2023

A Combinatorial Characterization of Online Learning Games with Bounded Losses

We study the online learnability of hypothesis classes with respect to a...
research
08/13/2020

Dynamic Complexity of Expansion

Dynamic Complexity was introduced by Immerman and Patnaik <cit.> (see al...

Please sign up or login with your details

Forgot password? Click here to reset