Adapting ranking SVM to document retrieval

define support vector machine and how to use svm in matlab and knowledge discovery with support vector machines
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Published Date:20-07-2017
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Introduction to Information Retrieval Introduction to Information Retrieval Support Vector Machines 1Introduction to Information Retrieval Today’s class  Intensive machine-learning research in the last two decades to improve classifier effectiveness  New generation of state-of-the-art classifiers: support vector machines (SVMs), boosted decision trees, regularized logistic regression, neural networks, and random forests  Applications to IR problems, particularly text classification SVMs: A kind of large-margin classifier Vector space based machine-learning method aiming to find a decision boundary between two classes that is maximally far from any point in the training data (possibly discounting some points as outliers or noise) 4 4Introduction to Information Retrieval Support Vector Machines  2-class training data  decision boundary → linear separator  criterion: being maximally far away from any data point → determines classifier margin  linear separator position defined by support vectors 5 5Introduction to Information Retrieval Why maximise the margin? Points near decision surface → uncertain classification decisions (50% either way). A classifier with a large margin makes no low certainty classification decisions. Gives classification safety margin w.r.t slight errors in measurement or doc. variation 6 6Introduction to Information Retrieval Why maximise the margin? SVM classifier: large margin around decision boundary  compare to decision hyperplane: place fat separator between classes  fewer choices of where it can be put  decreased memory capacity  increased ability to correctly generalize to test data 7 7Introduction to Information Retrieval Let’s formalise an SVM with algebra Hyperplane An n-dimensional generalisation of a plane (point in 1-D space, line in 2-D space, ordinary plane in 3-D space). Decision hyperplane (previously seen, page 278) Can be defined by:  intercept term b   normal vector w (weight vector) which is perpendicular to the hyperplane  All points x on the hyperplane satisfy: (1) 8 8Introduction to Information Retrieval Let’s formalise an SVM with algebra Preliminaries Consider a binary classification problem:   x are the input vectors i  y are the labels i  The x define a space of labelled points called input space. i For SVMs, the two data classes are always named +1 and −1, and the intercept term is always explicitly represented as b. The linear classifier is then: (2) A value of −1 indicates one class, and a value of +1 the other class. 9 9Introduction to Information Retrieval Functional Margin We are confident in the classification of a point if it is far away from the decision boundary. Functional margin  th The functional margin of the i example x w.r.t the hyperplane i The functional margin of a data set w.r.t a decision surface is twice the functional margin of any of the points in the data set with minimal functional margin  factor 2 comes from measuring across the whole width of the margin  But we can increase functional margin by scaling w and b.  We need to place some constraint on the size of the w vector. 10 10Introduction to Information Retrieval Geometric margin Geometric margin of the classifier: maximum width of the band that can be drawn separating the support vectors of the two classes. (3) The geometric margin is clearly invariant to scaling of parameters:   if we replace w by 5 w and b by 5b, then the geometric margin is  the same, because it is inherently normalized by the length of w. 11 11Introduction to Information Retrieval Linear SVM Mathematically Assume canonical distance Assume that all data is at least distance 1 from the hyperplane, then: (4) Since each example’s distance from the hyperplane is , the geometric margin is We want to maximize this geometric margin.  That is, we want to find w and b such that:  is maximized  For all 12 12Introduction to Information Retrieval Linear SVM Mathematically (cont.) Maximizing is the same as minimizing This gives the final standard formulation of an SVM as a minimization problem: Example  Find w and b such that: is minimized (because , and for all We are now optimizing a quadratic function subject to linear constraints. Quadratic optimization problems are standard mathematical optimization problems, and many algorithms exist for solving them (e.g. Quadratic Programming libraries). 13 13Introduction to Information Retrieval Recapitulation We start a training data set  The data set defines the best separating hyperplane  We feed the data through a quadratic optimization procedure to find this plane  Given a new point to classify, the classification function computes the projection of the point onto the hyperplane normal.  The sign of this function determines the class to assign to the point.  If the point is within the margin of the classifier, the classifier can return “don’t know” rather than one of the two classes.  The value of may also be transformed into a probability of classification 14 14Introduction to Information Retrieval Soft margin classification What happens if data is not linearly separable?  Standard approach: allow the fat decision margin to make a few mistakes  some points, outliers, noisy examples are inside or on the wrong side of the margin  Pay cost for each misclassified example, depending on how far it is from meeting the margin requirement Slack variable ξ : A non-zero value for ξ allows to not meet the i i margin requirement at a cost proportional to the value of ξ . i Optimisation problem: trading off how fat it can make the margin vs. how many points have to be moved around to allow this margin. The sum of the ξ gives an upper bound on the number of training i errors. Soft-margin SVMs minimize training error traded off against 15 15 margin.Introduction to Information Retrieval Multiclass support vector machines SVMs: inherently two-class classifiers.  Most common technique in practice: build C one-versus- rest classifiers (commonly referred to as “one-versus-all” or OVA classification), and choose the class which classifies the test data with greatest margin  Another strategy: build a set of one-versus-one classifiers, and choose the class that is selected by the most classifiers. While this involves building C(C − 1)/2 classifiers, the time for training classifiers may actually decrease, since the training data set for each classifier is much smaller. 16 16Introduction to Information Retrieval Multiclass support vector machines Better alternative: structural SVMs  Generalization of classification where the classes are not just a set of independent, categorical labels, but may be arbitrary structured objects with relationships defined between them  Will look at this more closely with respect to IR ranking next time. 17 17Introduction to Information Retrieval Text classification Many commercial applications  “There is no question concerning the commercial value of being able to classify documents automatically by content. There are myriad potential applications of such a capability for corporate Intranets, government departments, and Internet publishers.” Often greater performance gains from exploiting domain-specific text features than from changing from one machine learning method to another.  “Understanding the data is one of the keys to successful categorization, yet this is an area in which most Categorization tool vendors are extremely weak. Many of the ‘one size fits all’ tools on the market have not been tested on a wide range of content types.” 19 19Introduction to Information Retrieval Choosing what kind of classifier to use When building a text classifier, first question: how much training data is there currently available? Practical challenge: creating or obtaining enough training data Hundreds or thousands of examples from each class are required to produce a high performance classifier and many real world contexts involve large sets of categories.  None?  Very little?  Quite a lot?  A huge amount, growing every day? 20 20Introduction to Information Retrieval If you have no labeled training data Use hand-written rules Example IF (wheat OR grain) AND NOT (whole OR bread) THEN c = grain In practice, rules get a lot bigger than this, and can be phrased using more sophisticated query languages than just Boolean expressions, including the use of numeric scores. With careful crafting, the accuracy of such rules can become very high (high 90% precision, high 80% recall). Nevertheless the amount of work to create such well-tuned rules is very large. A reasonable estimate is 2 days per class, and extra time has to go into maintenance of rules, as the content of documents in classes drifts over time. 21 21Introduction to Information Retrieval If you have fairly little data and you are going to train a supervised classifier Work out how to get more labeled data as quickly as you can.  Best way: insert yourself into a process where humans will be willing to label data for you as part of their natural tasks. Example Often humans will sort or route email for their own purposes, and these actions give information about classes. Active Learning A system is built which decides which documents a human should label. Usually these are the ones on which a classifier is uncertain of the correct classification. 22 22Introduction to Information Retrieval If you have labeled data Reasonable amount of labeled data Use everything that we have presented about text classification. Preferably hybrid approach (overlay Boolean classifier) Huge amount of labeled data Choice of classifier probably has little effect on your results. Choose classifier based on the scalability of training or runtime efficiency. Rule of thumb: each doubling of the training data size produces a linear increase in classifier performance, but with very large amounts of data, the improvement becomes sub-linear. 23 23

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