  The paper presents new machine learning methods: signal composition, which
classifies time-series regardless of length, type, and quantity; and
self-labeling, a supervised-learning enhancement. The paper describes further
the implementation of the methods on a financial search engine system to
identify behavioral similarities among time-series representing monthly returns
of 11,312 hedge funds operated during approximately one decade (2000 - 2010).
The presented approach of cross-category and cross-location classification
assists the investor to identify alternative investments.
