Model Conditional Field Accreditation Gratis

one very important variant of Markov networks that is probably at this point more commonly used than other kinds and than anything that's not this type is what's called a conditional random field so conditional random field you can think of it as a something that looks very much like a mark of network but for a somewhat different purpose so let's think about what we are trying to do here this class our model is intended to deal with what we call task specific prediction that is where we have a set of input variables or observed variable X and we have a set of target variables that we're trying to predict Y and classic model is intended to be designed for those cases where we always have the same types of variables as the inter variables and the same types of variables as the targets and we're always trying to follow what is essentially the exact same problem or the exact same type of problem so let's think about that is let's think about two examples along the client so again going back to the example of image segmentation here we have the input is always a set of pixels with their values pixel values which we can process to produce more expressive features like histograms of colors and textures but this is sort of not relevant at this pointer but will be in a minute so my input X is pixel values and process features from those and my target variable Y is a class for every pixel so for example where by class I mean you know grass by cow water and so on and we're always trying to do we design them all so as to solve this problem of going from X to Y and we want to solve that bubble in some of the text processing or NLP problems that we discussed the the pics the input X are the word in a sentence and the output that we're trying to derive is for example the label of the word where again the label might be in this case things like person location organization and so on and once again this is always our input the words and the outer is always available so why what is the problem so fine so we want to solve this this kind of prediction problem why not use just a regular old graphical model in the same way that we've done so far let's think about what some of the issues might be too fast so imagine that we are trying to predict in this case CI which is the label of a particular super pixel in this case I and we're going to go ahead and process our features of that super pixel to provide a range of different color and texture histograms that that represent you know different ways of measuring the the appearance of the super pixel so in this case that's my Y this is a realization of the previous slide this is my target Y and these eyes down here are my X's so what's the problem with this suppose this is that these features that are often much more informative about about these about the class of the pixel are very correlated with each other so for example the the texture histograms which tell us sort of the directions in which lines go in a particular...