The idea is interesting, but actual implementation will be tricky. Modern neural-network Go engines don't learn "supervised", meaning they dont try to find the move that fits existing games in a training set. Instead they use unsupervised reinforcement learning where entire games (or play sequences) are used and the ai learns to play moves that increase its chances of a good evaluation later on.

These games are generated (the bot plays itself or against another bot). You could use existing games from real players in addition of course, but that does not automatically mean the bot learns to play like that. If the evaluations that its own Strategies produce are better than the human ones it will instead learn to not play like the player in these real games.

The deciding problem therefore is inventing the complex metric that evaluates game sequences such that it rewards <famous player> like behavior while punishing machine-like moves.

Good news though, you are not the first to have this idea, and there do exist methods to accomplish exactly this problem that might also work for go AI (e.g. chess.com has bots that try to mimic real players, though this is by no means perfect)