What does "no value is acceptable" in Machine Learning hypothesis representation I am new to machine learning. I am reading the book, where I have:
– Most general hypothesis
&lt; ? ? ? ? ? ? >
– Most specific hypothesis
&lt; Ø Ø Ø Ø Ø Ø >

in 
Sky AirTemp Humid Wind Water Forecast
&lt; Sunny ? ? Strong ? Same>

I understand that for a learning instance, "?" means it can take on any value, so the outcome of the hypothesis would be 1, as long as the specific attribute is met.

I am confused about "Ø". The book says any value is unacceptable.

Does that mean, if a hypothesis is &lt; ? ? ? ? ? Ø>, no matter what first 5 attributes are, since the last one is unacceptable, the outcome of the hypothesis will be 0?
If so, what is the difference between &lt; ? ? ? ? ? Ø>, and &lt; Ø Ø Ø Ø Ø Ø >?
Yes, that's right.  Those two hypotheses are equivalent, in that they assign the same classification to every possible instance.  Sometimes there are two different ways to represent the same thing.