Before revision:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
After revision:  From an on-line machine learning perspective, we first formulate online portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .