# HAMD

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AcronymDefinition
HAMDHamilton Rating Scale for Depression
HAMDHamming Distance
HAMDHelicopter Ambulance Medical Detachment
References in periodicals archive ?
Using greater value of w is shown to enhance the locality of two similar samples, since the average hamming distance between the keys, which generated using more shingles, is smaller; see Figure 4.4.
To account for eye rotation, the Hamming distance is computed for several different permutations of the bits corresponding to the different angles of rotations.
The Euclidean square distance coincides with the Lee distance and the Hamming distance over [Z.sub.2] and [Z.sub.3].
J = argmin d (F[bar]x),[m.sub.r], where d(.,.) is Hamming distance defined by (20), [m.sub.r] is the r-th row of M.
We are mainly concerned with polynomials over the two element finite field and where the distance measure is the Hamming distance between polynomials of the same degree.
In this case, input patterns that we want to classify are keywords set and not 0-1 binaries vectors, so Hamming distance can't be used, for this reason we have developed a particular kind of metrics that we can call "semantic metrics." We can use it both in the intrasystem elaboration process and in the intra-systems communication; in the latter case we can transmit the learning objects using XML-RPC protocol and the receiving system can analyze and classify the documents, processing semantic keywords labels through the algorithm we are going to explain.
Then, using a normalized Hamming distance, the fitness function f(t) = 1 - d(I, t(I)) is known to have a global maximum at 1, and may have several local maxima.
m_dist(D) = min{h I| [Exist] x [Epsilon] M(S, h), [Inverted A] d [Epsilon] D, 0 [is less than] CurrWt(d,/x)} m_ wt(D) = max min CurrWt(d, x) x [Epsilon] M (s,m_dist(D)) d [Epsilon] D MutScore(D) = max{0, [h.sub.max] - m_dist(D) + m_wt(D)} where [h.sub.max] bounds the maximum Hamming distance considered, CurrWt(d, x) applies CurrWt to d using x instead of S, and M(S, h) is the set of mutants of S at Hamming distance h.
The direct network on the other hand processes only those bits that need correction (one per cycle), thereby routing the packet in h cycles, where h = H(S, D) is the Hamming distance between S and D.
The normalized Hamming distance between [m.sub.1] and [m.sub.2] is defined as:
(ii) The Hamming distance can represent population diversity properly.
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