supervised learning for misuse signature detection

... Finally we compared our co-training performance with supervised learning For supervised learning, all labeled utterances except for the test set are used for training We used most confident selection ... been done in prosodic detection using unsupervised or semi -supervised methods Ananthakrishnan and Narayanan (2006) proposed an unsupervised algorithm for prosodic event detection This algorithm ... used to detect IPB and break index, not for accent detection that our feature extraction is performed at the syllable level This is straightforward for accent detection since stress is defined associated...

Ngày tải lên: 23/03/2014, 16:21

... Graph Based Semi -Supervised Learning for Entailment Ranking We formulate semi -supervised entailment rank scores as follows Let each data point in X = {x1 , , xn }, xi ∈ d represents information about ... Large-scale algorithms In In: SemiSupervised Learning, pages 333–341 MIT Press Nguyen Thanh Tri, Nguyen Minh Le, and Akira Shimazu 2006 Using semi -supervised learning for question classification In ... semi -supervised learning algorithm for web page classification In Proc Sixth Int Conf on Intelligent Systems Design and Applications Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani 2003 Semi-supervised...

Ngày tải lên: 20/02/2014, 07:20

Ngày tải lên: 08/03/2014, 01:20

... (Prob)’), performs competitively with both of the approaches that use our probabilistic learning model 998 10.6 Error Analysis Some errors are due to the variety of hedge forms For example, the learning ... co/self-training strategy for weakly supervised relation classification and (Chen et al., 2006) where a graph based algorithm called label propagation is employed to perform weakly supervised relation ... strategies is an avenue for future research There are a number of factors that make our formulation of hedge classification both interesting and challenging from a weakly supervised learning perspective...

Ngày tải lên: 23/03/2014, 18:20

... probabilities in Eq (3), for a given {K, M } candidate, we can compute the conditional probability of Expression (2) In the final step, optimization is performed to search for {K, M } that maximizes ... similarity features; and (iii) statistical annealing for optimization Learning Using Automatically Constructed Corpora This section presents our machine learning scheme to estimate the conditional probabilities ... the form of { f α } , we re-formulate the two conditional probabilities as ( ) ( ) Pr ({ f α } name1 = name2 ) Pr { fα } | Pi = Pj and Pr { f α } | Pi ≠ Pj = [Pr ({ f α } P1 = P2 ) The learning...

Ngày tải lên: 31/03/2014, 03:20

... testing frames for the different methods is shown in Table Note that there is no need for unusual event training data for our approach For the unsupervised HMM, there is no need for training data ... HTER (supervised 1) HTER (supervised 2) HTER (unsupervised) 0.45 0.4 0.35 0.3 0.25 iteration (b) Figure Results of visual unusual events detection audio-visual Method our method supervised supervised-2 ... intuitive primitives for simple cases, it is infeasible for complex events, in which primitives are difficult to define As an alternative, unsupervised approaches for unusual event detection have also...

Ngày tải lên: 19/02/2014, 18:20

... Word features: wi for i in {−2, −1, 0, +1, +2}, wi ∧ wi+1 for i in {−1, 0} • Tag features: wi for i in {−2, −1, 0, +1, +2}, ti ∧ ti+1 for i in {−2, −1, 0, +1} ti ∧ ti+1 ∧ ti+2 for i in {−2, −1, ... EACL Ando, R., & Zhang, T (2005) A highperformance semi -supervised learning method for text chunking ACL Honkela, T (1997) Self-organizing maps of words for natural language processing applications ... Weston, J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning ICML Liang, P (2005) Semi -supervised learning for natural language Master’s thesis,...

Ngày tải lên: 20/02/2014, 04:20

... and Y Bengio 2004 Semi -supervised learning by entropy minimization In NIPS T Grenager, D Klein, and C Manning 2005 Unsupervised learning of field segmentation models for information extraction In ... Figure 2: Accuracy of supervised and semi -supervised learning methods for fixed numbers of labeled tokens Training a GE model with only labeled features significantly outperforms traditional log-likelihood ... amount of work on semi -supervised learning with small amounts of fully labeled data (see Zhu (2005)) However there has been comparatively less work on learning from alternative forms of labeled resources...

Ngày tải lên: 20/02/2014, 09:20

... Lightly -Supervised Attribute Extraction NIPS 2007 Workshop on Machine Learning for Web Search A Carlson, J Betteridge, R.C Wang, E.R Hruschka Jr, and T.M Mitchell 2010 Coupled Semi -Supervised Learning ... knowledge base The knowledge base harvests information from many open data sets (for instance Wikipedia and MusicBrainz), as well as from user contributions For our current purposes, we can think of ... Create a node for each unique cell value • Create a node for each unique property name, where unique property name is obtained by prefixing the unique table ID to the property name For example,...

Ngày tải lên: 07/03/2014, 22:20

... they are also very informative for supervised learning Their use is important to further boost the performance gain offered by our method ∑ this purpose, we define φ(x, y) For M as φ(x, y) = m=1 ... finishing supervised learning Finally, we compared the performance in terms of the number of active features in the model given by supervised learning We note here that the number of active features for ... determined by supervised learning, m=1 we can calculate wm in a preliminary step before the test phase Thus, our method also takes the form of a linear model The number of features for our method...

Ngày tải lên: 07/03/2014, 22:20

... version, our first supervised version, our supervised version with our special rules, our supervised version with special rules and additional constraints, our unsupervised version, our supervised version ... programs, one for the system’s response as it executes the program, and a third for output tables and charts 294 Figure 1: Compression Examples humans K&M supervised supervised with extra rules supervised ... enforcing some deletion constraints improves grammaticality We also show that it is possible to perform an unsupervised version of the compression task, which performs remarkably well Our semisupervised...

Ngày tải lên: 17/03/2014, 05:20

... semi -supervised learning, here we not include semi -supervised learning as a baseline A multi-task transfer learning solution We now present a multi-task transfer learning solution to the weakly -supervised ... classifier for any relation type Another existing solution to weakly -supervised learning problems is semi -supervised learning, e.g bootstrapping However, because our proposed transfer learning ... transfer learning and domain adaptation for named entity recognition Dredze and Crammer (2008) proposed an online method for multi-domain learning and adaptation Multi-task learning is another learning...

Ngày tải lên: 23/03/2014, 16:21

... select informative examples only A combination of active and semi -supervised learning has first been proposed by McCallum and Nigam (1998) for text classification Committeebased AL is used for the ... sentences are fully manually labeled We refer to this AL mode as fully supervised Active Learning (FuSAL) 3.2 Semi -Supervised Active Learning In the sequence labeling scenario, an example which, as ... sophisticated utility functions for sequence labeling We have chosen this straightforward one for simplicity and because it has proven to be very effective (Settles and Craven, 2008) For the sequence labeling...

Ngày tải lên: 23/03/2014, 16:21

... with Active Learning for Word Sense Disambiguation Proc of ACL 2007, 49-56 Chen, J., Schein, A., Ungar, L., and Palmer, M 2006 An Empirical Study of the Behavior of Active Learning for Word Sense ... no concern in most Web mining applications 2.1 Classifier For our experiment, we use naive Bayes classifiers as learning algorithm In performing WSD, the sense “s” is assigned to an example characterized ... Nigam, K 1998 Employing EM and Pool-Based Active Learning for Text Classification Proceedings of the Fifteenth international Conference on Machine Learning, 350-358 Nigam, K., McCallum, A., Thrun,...

Ngày tải lên: 23/03/2014, 17:20

... already high Therefore the additional information discovered by SVD-ASO appears crucial to achieve appreciable improvements Semi -supervised Learning Method For semi -supervised learning, the idea ... predictor for problem takes the following form  ĩà ĩ ã Âĩ  This work uses a linear formulation of structural learning We rst briey review a standard linear prediction model and then extend it for ... optimization algorithm for this extension is essentially the same as SVD-ASO in Figure 1, but with the SVD step performed separately for each group See (Ando and Zhang, 2004) for the precise formulation...

Ngày tải lên: 23/03/2014, 19:20

... event detection In both scenarios, we are only given class labels for the videos We use the Olympic Sports dataset [16] and the 2011 TRECVID Multimedia Event Detection (MED) dataset [18] For both ... i=1 where we consider linear classifiers of the form: fw (v) = max w · Φ(v, h) h (8) The latent variables h in the classifier are solved for by performing MAP inference on the example v to find the ... construct the feature vector Φ(v, h) for an example v as follows For the wa parameters we sum the feature histograms that are assigned to each state, and for the wt and wd parameters we count...

Ngày tải lên: 30/03/2014, 16:20

Ngày tải lên: 30/03/2014, 17:20

... the absolute values for label propagation Figure 3: Grid search on a unit 2-simplex with k = The complexity of this search procedure is O(k n ) for k bins and n parameters For problems with small ... labels are assigned uniformly at random (UNIFORM) and 2) where labels are assigned according the empirical prior distribution (PRIOR) The baseline accuracies are shown in Table UNIFORM PRIOR Na¨ve ... proposed alternatives to scale up grid search for large problem instances Our results show a significant performance improvement over the baseline and this performance is further improved by parameter...

Ngày tải lên: 30/03/2014, 21:20

... 4] for a discussion The performance for different network architectures is shown in Section Table shows network performance for different numbers of input variables and Table shows performance for ... frequency axis For each of these, we evaluated model performance for different window widths Table shows the results for these tests The best performance was achieved with the constant-Q transform, but ... results For a sampling rate of 22050 Hz, this yields ∼ 1000 (22050/20) input values per frame for a supervised learning algorithm As it is commonly done, we decided to use a time-space transform...

Ngày tải lên: 22/06/2014, 23:20

... problems, machine learning algorithms can be broadly categorized into three paradigms: supervised learning, unsupervised learning and semi -supervised learning Supervised learning is for the case where ... Semi -supervised learning is a compromise between supervised learning and unsupervised learning, in which a few labeled and a large amount of unlabeled data are available Hence, semi -supervised learning ... where the labels of empirical data are given, for example, supervised classification and supervised regression By contrast, unsupervised learning is for the case where the labels of empirical data...

Ngày tải lên: 10/09/2015, 15:47