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Bio-medical Ontologies Maintenance and Change Management

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202 I.R. Godínez et al.<br />

2. The V l matrices are built according to the learning phase of the alphabeta<br />

heteroassociative multimemories type Max. The output patterns of<br />

these memories are denoted as y.<br />

3. The Λ l matrices are built according to the learning phase of the alphabeta<br />

heteroassociative multimemories type Min. The output patterns of<br />

these memories are denoted as yz.<br />

RECALLING PHASE<br />

1. A pattern to be classified is presented to the learning matrix type Max<br />

<strong>and</strong> the resulting vector is known as ymax.<br />

2. The same pattern is presented to the learning matrix type Min <strong>and</strong> the<br />

resulting vector is known as ymin.<br />

3. The vector ymin is negated to obtain the ˜ymin vector.<br />

4. Apply the OR binary operation between the components of the ymax<br />

<strong>and</strong> ˜ymin. The resulting vector is known as total class vector y.<br />

5. The first k components from y, corresponding to the first class, are added;<br />

the second k components from y, corresponding to the second class, are<br />

added too, <strong>and</strong> so on, until we obtain as many values as classes we have.<br />

6. The greater value(s) is(are) taken as the correct class(es).<br />

7. For those cases in which there are more than one correct class elected, it<br />

will not be possible determine to which class the pattern belongs, so it<br />

will be considered as incorrect classification.<br />

Example 3. Let the input patterns be divided in three different classes, x1 ,x2 to class 1 x3 ,x4 to class 2 <strong>and</strong> x5 ,x6 to class 3 :<br />

x 1 ⎛ ⎞<br />

1<br />

⎜ 0 ⎟<br />

⎜<br />

= ⎜ 1 ⎟<br />

⎜ 1 ⎟ ,x<br />

⎟<br />

⎝ 0 ⎠<br />

0<br />

2 ⎛ ⎞<br />

0<br />

⎜ 1 ⎟<br />

⎜<br />

= ⎜ 0 ⎟<br />

⎜ 0 ⎟ ,x<br />

⎟<br />

⎝ 0 ⎠<br />

1<br />

3 ⎛ ⎞<br />

1<br />

⎜ 0 ⎟<br />

⎜<br />

= ⎜ 0 ⎟<br />

⎜ 1 ⎟ ,<br />

⎟<br />

⎝ 0 ⎠<br />

1<br />

x 4 ⎛ ⎞<br />

1<br />

⎜ 1 ⎟<br />

⎜<br />

= ⎜ 0 ⎟<br />

⎜ 1 ⎟ ,x<br />

⎟<br />

⎝ 1 ⎠<br />

1<br />

5 ⎛ ⎞<br />

1<br />

⎜ 0 ⎟<br />

⎜<br />

= ⎜ 0 ⎟<br />

⎜ 1 ⎟ ,x<br />

⎟<br />

⎝ 1 ⎠<br />

1<br />

6 ⎛ ⎞<br />

1<br />

⎜ 1 ⎟<br />

⎜<br />

= ⎜ 1 ⎟<br />

⎜ 0 ⎟<br />

⎝ 0 ⎠<br />

0<br />

<strong>and</strong> the corresponding output patterns yμ in one-hot <strong>and</strong> yzμ in zero-hot<br />

codifications. Then the fundamentals sets are expressed as follow:<br />

�� 1 1<br />

x ,y � , � x 2 ,y 2� , � x 3 ,y 3� , � x 4 ,y 4� , � x 5 ,y 5� , � x 6 ,y 6��<br />

�� 1 1<br />

x ,yz � , � x 2 ,yz 2� , � x 3 ,yz 3� , � x 4 ,yz 4� , � x 5 ,yz 5� , � x 6 ,yz 6��

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