In each step the LZSS algorithm sends either a character or a <position, length> pair. Among these, perhaps character "e" appears more frequently than "x", and a <position, length> pair of length 3 might be commoner than one of length 18, say. Thus, if we encode the more frequent in fewer bits and the less frequent in more bits, the total length of the encoded text will be diminished. This consideration suggests that we use Huffman or arithmetic coding, preferably of adaptive kind, along with LZSS.
apriori java 實現 * A program to find association rules with the apriori algorithm (Agrawal et al. 1993).<br> * Other than the standard apriori algorithm, this program enable to find<br> * apriori all relation.
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
