<p>One major task of computational learning theory is to try to handle questions like this for specific classes.  In general, if you assume no structure on the hypothesis space, you basically have no choice but to look at each hypotheses individually.  On the other hand, if there is a lot of structure, eg. if you assume the hypotheses are linear separators, say in $\mathcal{R}^2$, you can see that it's possible to be quite efficient even though there are infinitely many linear separators!</p>

<p>The same thing happens for trying to find the ERM (empirical risk minimizing) hypothesis in a class.  This is important for PAC learning. Sometimes it's easy, sometimes it's not.</p>

CC BY-SA 3.0
2013-11-25T17:11:06.087Z
I am learning a DAG model (similar to Bayesian Networks). So each hypothesis represents a possible DAG. I should look deeply in finding an efficient method to this. Thanks!
seteropere
CC BY-SA 3.0
2013-11-25T21:12:24.837Z
is it valid practice to choose $m$ hypotheses from $\mathcal{H}$ to compute the region of uncertainty? I am making up a representative measure for choosing them
seteropere
CC BY-SA 3.0
2013-11-25T21:46:52.757Z
In general no.  Imagine your class has $n-1$ hypotheses that completely agree and $1$ that's very different.  Via sampling, before you even see any data, you are likely to think there is no region of uncertainty, whereas the reality could be very different.

But whether this is a good strategy or not heavily depends on your goal.  For instance, if for a given point, you want to measure how "uncertain" hypotheses are about it, and you're okay with some error, then sampling is fine.

These are of course two extremes...
Lev Reyzin
CC BY-SA 3.0
2013-11-25T16:39:33.050Z
2013-11-25T16:39:33.050Z
Lev Reyzin
<p>Given your edit, the question now looks trivial.  The structure on $\mathcal{O}$ is irrelevant.  Basically,  you are given a set $\mathcal{O}$ and a set of pairs $a_i,b_i$ with the promise that $a_i &lt; b_i$ for some total order $&lt;$.  You want to find all pairs $a',b'$ such that there exists some total orders $&lt;_1,&lt;_2$ that are consistent with all the examples and such that $a' &lt;_1 b'$ and $a' &gt;_2 b'$.</p>

<p>Assuming I've got all that right, that's an easy problem to solve, computationally.  Basically, build a DAG with an edge $a_i \to b_i$ for each $i$.  Compute the transitive closure of the DAG.  Now, if there's an edge $a' \to b'$ in the transitive closure, then you know $a' &lt; b'$ in all possible orders.  On the other hand, for any pair of vertices $a',b'$ that are not connected by an edge in the transitive closure, you could have either $a' &lt; b'$ or $a' &gt; b'$.  So, that's the region of uncertainty: the set of pairs of vertices $a',b'$ that are not connected by an edge in the transitive closure of this DAG.</p>

<p>If I haven't got all that right, you might need to provide a clearer statement of the problem in the question.</p>

CC BY-SA 3.0
2013-11-29T19:15:58.577Z
The target order may be partial. I do exactly what you stated. My issue is in how to choose a set of representative hypotheses from the set of all possible DAGs consistent with the current examples. I can create the representative hypotheses artificially but I am not sure whether its a valid practice.
seteropere
CC BY-SA 3.0
2013-11-29T19:50:36.857Z
@seteropere, you asked how to compute the region of uncertainty.  I've answered that question: I've described a way to do that.  (You don't need to choose a set of representative hypotheses to compute the region of uncertainty.  I've answered the question you asked, so if what you asked isn't what you really wanted, you might need to edit the question?)
D.W.
CC BY-SA 3.0
2013-11-29T21:48:16.567Z
It seems I am missing something obvious here. I thought I cannot compute disagreements without choosing a subset of hypotheses.
seteropere
CC BY-SA 3.0
2013-11-29T18:28:05.570Z
2013-11-29T18:28:05.570Z
D.W.
<p>Given a class of hypothesis $\mathcal{H}$ representing the set of all consistent hypotheses with the examples seen so far, how to compute the region of uncertainty? The region of uncertainty is defined as all points where there are two or more hypotheses disagree on its labelling. </p>

<p>I am representing $\mathcal{H}$ as two hypotheses: most specific (MS) and most general (MG) (as described <a href="http://en.wikipedia.org/wiki/Version_space" rel="nofollow">here</a> ). $\mathcal{H}$ consists of these two and anything in between. Clearly, enumerating all possible hypotheses is infeasible due to the size of $\mathcal{H}$. To put it in one word, how to compute disagreements in practice?  </p>

<p>EDIT: </p>

<p>sorry for not being specific. I am trying to learn a graphical model similar to bayesian networks but instead of probabilities I have order relation.  </p>

<p>I have a set of variables $V=\{v_1,v_2,...,v_n\}$ where each variable has a set of possible values (its domain). $V$ defines an outcome space $\mathcal{O}$ (the set of all possible assignments over $V$ from their domain values). For example, if $A=\{1,2\}$ and $B=\{3,4\}$ we have $\mathcal{O}=\{(1,3),(1,4),(2,3),(2,4)\}$. The input space $X$ is the set of all pairs of outcomes from $\mathcal{O}$. Each example $x$ consist of pairs of outcomes $a$,$b\in \mathcal{O}$ (denoted as $x[a]$,$x[b]$ respectively). The target function is a strict order relation $\succ$ over $\mathcal{O}$. I am adopting the active learning paradigm. I first choose $\mathcal{U}$ unlabelled examples, then ask the oracle to label an example $x\in \mathcal{U}$. $h(x)=+$ if $x[a]$ is better than $x[b]$ otherwise $-$. The hypothesis class $\mathcal{H}$ is the set of all possible strict (partial/total) orders consistent with the examples seen so far.  My main concern is how to select representative hypotheses from $\mathcal{H}$ </p>

CC BY-SA 3.0
2013-11-28T19:26:29.177Z
I think your problem is too general. You need to give more specification on the properties of your hypothesis characterization, and your inputs
Daniel
CC BY-SA 3.0
2013-11-28T21:03:36.600Z
@Daniel I have edited the question.
seteropere
<machine-learning>
How to compute the disagreement between hypotheses
seteropere
# How to compute the disagreement between hypotheses

(Asked by: username_0 on 2013-11-25)

Given a class of hypothesis $\mathcal{H}$ representing the set of all consistent hypotheses with the examples seen so far, how to compute the region of uncertainty? The region of uncertainty is defined as all points where there are two or more hypotheses disagree on its labelling.

I am representing $\mathcal{H}$ as two hypotheses: most specific (MS) and most general (MG) (as described [here](http://en.wikipedia.org/wiki/Version_space) ). $\mathcal{H}$ consists of these two and anything in between. Clearly, enumerating all possible hypotheses is infeasible due to the size of $\mathcal{H}$. To put it in one word, how to compute disagreements in practice?

EDIT:

sorry for not being specific. I am trying to learn a graphical model similar to bayesian networks but instead of probabilities I have order relation.

I have a set of variables $V=\{v\_1,v\_2,...,v\_n\}$ where each variable has a set of possible values (its domain). $V$ defines an outcome space $\mathcal{O}$ (the set of all possible assignments over $V$ from their domain values). For example, if $A=\{1,2\}$ and $B=\{3,4\}$ we have $\mathcal{O}=\{(1,3),(1,4),(2,3),(2,4)\}$. The input space $X$ is the set of all pairs of outcomes from $\mathcal{O}$. Each example $x$ consist of pairs of outcomes $a$,$b\in \mathcal{O}$ (denoted as $x[a]$,$x[b]$ respectively). The target function is a strict order relation $\succ$ over $\mathcal{O}$. I am adopting the active learning paradigm. I first choose $\mathcal{U}$ unlabelled examples, then ask the oracle to label an example $x\in \mathcal{U}$. $h(x)=+$ if $x[a]$ is better than $x[b]$ otherwise $-$. The hypothesis class $\mathcal{H}$ is the set of all possible strict (partial/total) orders consistent with the examples seen so far. My main concern is how to select representative hypotheses from $\mathcal{H}$

**Tags:** machine-learning

> **Comments:**
> **username_1**: I think your problem is too general. You need to give more specification on the properties of your hypothesis characterization, and your inputs  
> **username_0**: @username_1 I have edited the question.  

---

## 2 answers

### Answer by username_2 on 2013-11-25. Score: 6 | Accepted answer

One major task of computational learning theory is to try to handle questions like this for specific classes. In general, if you assume no structure on the hypothesis space, you basically have no choice but to look at each hypotheses individually. On the other hand, if there is a lot of structure, eg. if you assume the hypotheses are linear separators, say in $\mathcal{R}^2$, you can see that it's possible to be quite efficient even though there are infinitely many linear separators!

The same thing happens for trying to find the ERM (empirical risk minimizing) hypothesis in a class. This is important for PAC learning. Sometimes it's easy, sometimes it's not.

> **Comments:**
> **username_0**: I am learning a DAG model (similar to Bayesian Networks). So each hypothesis represents a possible DAG. I should look deeply in finding an efficient method to this. Thanks!  
> **username_0**: is it valid practice to choose $m$ hypotheses from $\mathcal{H}$ to compute the region of uncertainty? I am making up a representative measure for choosing them  
> **username_2**: In general no.  Imagine your class has $n-1$ hypotheses that completely agree and $1$ that's very different.  Via sampling, before you even see any data, you are likely to think there is no region of uncertainty, whereas the reality could be very different.  But whether this is a good strategy or not heavily depends on your goal.  For instance, if for a given point, you want to measure how "uncertain" hypotheses are about it, and you're okay with some error, then sampling is fine.  These are of course two extremes...  

---

### Answer by username_3 on 2013-11-29. Score: 3

Given your edit, the question now looks trivial. The structure on $\mathcal{O}$ is irrelevant. Basically, you are given a set $\mathcal{O}$ and a set of pairs $a\_i,b\_i$ with the promise that $a\_i < b\_i$ for some total order $<$. You want to find all pairs $a',b'$ such that there exists some total orders $<\_1,<\_2$ that are consistent with all the examples and such that $a' <\_1 b'$ and $a' >\_2 b'$.

Assuming I've got all that right, that's an easy problem to solve, computationally. Basically, build a DAG with an edge $a\_i \to b\_i$ for each $i$. Compute the transitive closure of the DAG. Now, if there's an edge $a' \to b'$ in the transitive closure, then you know $a' < b'$ in all possible orders. On the other hand, for any pair of vertices $a',b'$ that are not connected by an edge in the transitive closure, you could have either $a' < b'$ or $a' > b'$. So, that's the region of uncertainty: the set of pairs of vertices $a',b'$ that are not connected by an edge in the transitive closure of this DAG.

If I haven't got all that right, you might need to provide a clearer statement of the problem in the question.

> **Comments:**
> **username_0**: The target order may be partial. I do exactly what you stated. My issue is in how to choose a set of representative hypotheses from the set of all possible DAGs consistent with the current examples. I can create the representative hypotheses artificially but I am not sure whether its a valid practice.  
> **username_3**: @username_0, you asked how to compute the region of uncertainty.  I've answered that question: I've described a way to do that.  (You don't need to choose a set of representative hypotheses to compute the region of uncertainty.  I've answered the question you asked, so if what you asked isn't what you really wanted, you might need to edit the question?)  
> **username_0**: It seems I am missing something obvious here. I thought I cannot compute disagreements without choosing a subset of hypotheses.  

---

