Abstract:
:Two different neural implementations of support vector machines are described and applied to one-shot trainable pattern recognition. The first model is based on oscillating associative memory and is mapped to the olfactory system. The second model is founded on competitive queuing memory originally employed for generating motor action sequences in the brain. Both models include forward pathways where a stream of support vectors is evoked from memory and merges with sensory input to produce support vector machine classifications. Misclassified events are imprinted as new support vector candidates. Support vector machine weights are tuned by virtual experimentation in sleep. Recalled training examples masquerade as sensor input and feedback from the classification process drives a learning process where support vector weights are optimized. For both support vector machine models it is demonstrated that there is a plausible evolutionary path from a simple hard-wired pattern recognizer to a full implementation of a biological kernel machine. Simple and individually beneficial modifications are accumulated in each step along this path. Neural support vector machines can apparently emerge by natural processes.
journal_name
Adv Exp Med Bioljournal_title
Advances in experimental medicine and biologyauthors
Jändel Mdoi
10.1007/978-1-4614-0164-3_16subject
Has Abstractpub_date
2011-01-01 00:00:00pages
193-207eissn
0065-2598issn
2214-8019journal_volume
718pub_type
杂志文章abstract::In attempts to correlate metastatic potential with specific properties of tumor cells, homogeneous subpopulations, which are endowed with low or high metastatic potential, have been selected from Lewis lung carcinoma (3LL). In particular, since cell surface constituents are possibly involved in the metastatic process,...
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