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Our goal is to enable machines to learn directly from sensory input streams. The learning machine does not require human teacher to specify any content-level rule. Such a capability requires a fundamentally new way of addressing the learning problem, one that unifies learning and performance phases and requires a systematic self-organization capability. The presented approach enables the system to self-organize its internal representation, and uses a systematic way to automatically build multi-level representation. In the experiments presented, we study the behavior of the method for automatic state self-organization and automatic level building that involves two levels. We test the algorithm for the problem of face recognition under a simple but important discourse scenario-a primary mode of our goal for human-machine interactive learning. |
