General-to-specific ordering of hypotheses

Many learning algorithms for concept learning organize the search through the hypothesis space by relying on a general-to-specific ordering of hypotheses. By taking advantage of this naturally occurring structure over the hypothesis space, we can design learning algorithms that exhaustively search even infinite hypothesis spaces without explicitly enumerating every hypothesis.

Consider two hypotheses,  h1 = (Sunny, ?, ?, Strong, ?, ?) and - h2 = (Sunny, ?, ?, ?, ?, ?)
By looking at the 2 hypothesis representations, the hypothesis h2 has fewer constraints on attributes than hypothesis h1. The sets of instances that are classified positive by hl will be less than sets of instances that are classified positive by h2 as h2 imposes fewer constraints on the instance.

In other words, we can also say that, any instance classified positive by hl will also be classified positive by h2.

Therefore, we say that h2 is more general than hl. In reverse, we can also say that, h1 is more specific than h2.

Lets formalize this concept. Hypothesis h1 and h2 classifies the instance x as positive can written as h1(x) = 1 and h2(x) = 1. Now h2 is more general than h1, so it can be written as if h1(x) = 1 implies h2(x) = 1. In symbols ..

Now lets describe a definition for more-general-than-or-equal-to —

In similar way, more-specific-than, also be defined as below.

By now, we understood the kind of relationship could exist between 2 hypothesis like more-general-than-equal-to or more-general-than. Lets illustrate this relationships using the below figure.

• In the figure, the box on the left represents the set X of all instances, the box on the right the set H of all hypotheses
• Each hypothesis corresponds to some subset of X, that is, the subset of instances that it classifies positive. In the figure, there are 2 instances x1 and x2, and 3 hypothesis h1, h2 and h2.
• h1 classifies — x1, h2 classifies — x1 and x2, and h3 classifies — x1. This indicates, h2 is more-general-than h1 and h3.
• The arrows connecting hypotheses represent the more-general-than relation, with the arrow pointing toward the less general hypothesis.
• Note the subset of instances characterized by h2 subsumes the subset characterized by hl , hence h2 is more-general–than h1

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MFCS
COA
PL-CG
DBMS
OPERATING SYSTEM
SOFTWARE ENG
DSA
TOC-CD
ARTIFICIAL INT

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