learning algorithms in a bootstrap paradigm, the study examined features classification with binary class attributes in a bootstrap paradigm. Support Vector Machine, k-Nearest Neighbour, Random Forest, rpart, Artificial Neural Network and Naïve Bayes learning algorithms were compared. Accuracy, Prediction error, Sensitivity and Specificity were used as assessment criteria of the classifier after tuning to have minimum cost. The study therefore sample the training set and classifying each of the training set, the summary of the prediction error was obtained based on the testing dataset, the study showed that artificial neural network outperformed other learning algorithms with respect to accuracy criterion whereas the celebrated support vector machine performed poorly amongst the learning algorithms considered, the study depicted that artificial neural network outperformed other learning algorithms with the least misclassification error. The study depicted that K nearest neighbour outperformed other learning algorithms with highest sensitivity while ANN outperformed other learning algorithms with highest specificity. This study affirmed that there would be need to use more than a learning algorithm when there are irrelevant features in the data sets.
In such way the centroid of all four clusters are calculated. The final step involves the repartitioning of the image into the clusters. The distance between an individual pixel of the image and the centroids of the four clusters are calculated. The minimum of the four distances is determined and the pixel is moved to the corresponding cluster. The centroid of the cluster is updated every time a pixel is moved into the cluster. This occurs iteratively until there are no more pixels left in the image to repartition. The four clusters are then obtained and are subjected to get their respective Histogram to determine the two clusters having more relevant information. This is done by executing the number of occurrences of each pixel value of the particular cluster. Then Probability Density Function of each pixel in the cluster and thereby using the extracted values to train the BPN-FF classifier.
We also evaluate our selection criteria on the evaluation of Surdeanu et al. (2012), both initial- ized with Mintz++ (Figure 7) and with the super- vised classifier (Figure 6). These results mirror those in the end-to-end evaluation; when initial- ized with the supervised classifier the high dis- agreement (High JS) and sampling proportional to disagreement (Sample JS) criteria clearly outper- form both the base MIML-RE model as well as the uniformly sampling criterion. Using the an- notated examples only during training yielded no perceivable benefit over the base model (Figure 7). Supervised Relation Extractor The examples collected can be used to directly train a supervised classifier, with results summarized in Table 4. The most salient insight is that the performance of the
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4 Concluding Remarks and Future Work This work presents a system that first identifies rel- evant pairs of concepts in EHRs by means of a shallow analysis and next examines all the pairs by an inferred supervised classifier to determine if a given pair represents a cause-effect event. A rel- evant contribution of this work is that we extract events occurring between concepts that are in dif- ferent sentences. In addition, this is one of the first works on medical event extraction for Spanish.
Abstract— Measuring the similarity between documents is an important operation in the text processing field. Text categorization (also known as text classification, or topic spotting) is the task of automatically sorting a set of documents into categories from a predefine set . TEXT categorization (TC) is the task of automatically classifying unlabeled natural language documents into a predefined set of semantic categories . The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization . The traditional term weighting methods borrowed from information retrieval(IR), such as binary, term frequency (tf), tf:idf, and its various variants, belong to the unsupervised term weighting methods as the calculation of these weighting methods do not make use of the information on the category membership of training documents. Generally, the supervised term weighting methods adopt this known information in several ways. Therefore, the fundamental question arise here, “Does the difference between supervised and unsupervised term weighting methods have any relationship with different learning algorithms?”, and if we consider normalized term frequency instead of term frequency along with relevant frequency the new method will be ntf.rf but will this new method is effective for text categorization? So we would like to answer these questions by implementing new supervised and unsupervised term weighing method (ntf.rf). The proposed TC method will use a number of experiments on two benchmark text collections 20NewsGroups and Reuters. Proposed system will use term weighting methods with preprocessing, so it will not requires labeled data and with the help of this, automatically results are improved in the form of precession, recall and accuracy. Proposed system improved the accuracy as compared to previous work and in that, supervised classifier is having more accuracy than unsupervised classifier.
The state-of-the-art approach treats the task of spam detection as a text categorization prob- lem and was first introduced by Jindal and Liu (2009) who trained a supervised classifier to dis- tinguish duplicated reviews (assumed deceptive) from original ones (assumed truthful). Since then, many supervised approaches have been proposed for spam detection. Ott et al. (2011) employed standard word and part-of-speech (POS) n-gram features for supervised learning and built a gold − standard opinion dataset of 800 reviews. Lim et al. (2010) proposed the inclusion of user behavior- based features and found that behavior abnormali- ties of reviewers could predict spammers, without using any textual features. Li et al. (2011) care- fully explored review-related features based on content and sentiment, training a semi-supervised classifier for opinion spam detection. However, the disadvantages of standard supervised learning methods are obvious. First, they do not gener- ally provide readers with a clear probabilistic pre-
Our future work will continue to develop tech- niques for addressing the challenges posed by ex- tending distant supervision to new types of IE tasks, and the refinement of our techniques. Specifically, it is still unclear how the number of seed instances for semi-supervised relabeling impacts TSF perfor- mance and why slot level performance is variable when the number of seed examples is similar. Also, we used a random set of seed examples for self- training and it is possible that learning from certain types of instances may prove more beneficial and that more iterations in the self-training process may continue to improve the accuracy of training labels and overall system performance.
Another family of methods learned the final classifier by using positive examples dataset and all examples of the unlabeled dataset. Li et al. (Li et al., 2009) studied PU learning in the data stream environment, they proposed a PU learn- ing LELC (PU Learning by Extracting Likely positive and negative micro-Clusters) for docu- ment classification, they assume that the exam- ples close together shared the same labels. Xi- ao et al. (Xiao et al., 2011) proposed a method, called SPUL (similarity-based PU learning), the local similarity-based and global similarity-based mechanisms are proposed to generate the similar- ity weights for the easily mislabeled examples, respectively. Experimental results show global SPUL generally performs better than local SPUL. In this paper, a novel PU learning (MPIPUL) is proposed to identify deceptive reviews.
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Also this paper shows the experimental result of standard deviation and Markov Classifier using limited parameter for their analysis and the performance evaluation stated, among the other missing value imputation techniques, the proposed method produce accurate result. In future, it can be expanded to handle categorical attributes and it can be substituted by other supervised machine learning techniques.
The task has another property which renders it problematic, and which prompted us to develop the discriminative training procedure described in this paper. Summarization, by definition, aims for brevity. This means that in any dataset the number of positive instances will be much smaller than the number of negative instances. Given enough data, balance could be restored by discarding negative in- stances. This, however, was not an option in our case: a moderate amount of manually labeled data had been produced and about one third would have had to be discarded to achieve a balance in the dis- tribution of class labels. This would have eliminated precious supervised training data, which we were not prepared to do.
Ping Liu et al.(2014) proposed a Boosted Deep Belief Network (BDBN) in the framework of multiple layered unified loopy. A training process for the face expression has been performed in the three individual stages: feature learning, selection, and the feature classiﬁer based data construction. These features will be trained and features are extracted in the extreme manner by using the dataset of the initial images. By using the BDBN framework, the feature set, generally used to classify the main characteristics of the expression of human facial related appearance and also the shape changes, boosted strong classifier by these feature set in the statistical way.
In this paper, we propose a general approach to modeling such constraints. In particular, we show that many types of constraints can be modeled by specifying the desired behavior of random walks through a graph of classifiers. In the graph, nodes correspond to relational conditions on small subsets of the data, and edges are annotated by feature vec- tors. Feature weights, combined with the feature vector at each edge and a non-linear postprocessing step, define a weighting of edges in the graph, and hence a transition function for a random walk. We will argue that traditional supervised classification tasks, as well as many natural SSL heuristics, can be approximated by specifying the desired outcome of walks through this graph.
Abstract— Real time Sentiment analysis is a subfield of Natural Language Processing concerned with the determination of opinion and subjectivity in a text, which has many applications. In this paper, classifiers for sentiment analysis of user opinion towards through comments and tweets sing Support Vector Machine (SVM) is described. The goal is to develop a classifier that performs sentiment analysis, by labeling the users comment to positive or negative. The extremely sparse text of tweets also brings down the performance of a sentiment classifier. In this paper, we propose a semi-supervised topic-adaptive sentiment classification (TASC) model, which starts with a classifier, built on common features and mixed labeled data from various topics. It minimizes the hinge loss to adapt to unlabeled data and features including topic-related sentiment words, authors’ sentiments and sentiment connections derived from ―@‖ mentions of tweets, named as topic-adaptive features. Text and non-text features are extracted and naturally split into two views for co-training. The TASC learning algorithm updates topic-adaptive features based on the collaborative selection of unlabeled data, which in turn helps to select more reliable tweets to boost the performance. We also design the adapting model along a timeline (TASC-t) for dynamic tweets. It also beats those semi-supervised learning methods without feature adaption. Finally, with timeline visualization of ―river‖ graph, people can intuitively grasp the ups and downs of sentiments’ evolvement, and the intensity by color gradation.
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Based on the accuracy assessment of the ex- tracted land cover maps from Landsat 8 images in summer 2014, the overall accuracy and Kappa co- efficient were 94.8 and 0.90, respectively (Table 6), which represented the overall error of ± 5.2% that would be an acceptable estimated error. It is worth saying that the overall accuracies in the previous studies on the same study area using ML classi- fier were about 80 and 96% for 2003 and 2008, re- spectively (Mirakhorlou 2003; Mirakhorlou, Akhavan 2008). Salman Mahini et al. (2012), who extracted an eastern Hyrcanian forest map us- ing the classification method and ML classifier on Landsat ETM+ images in 2010, obtained the over- all accuracy and Kappa coefficient of 91% and 0.70, respectively. Rezaee et al. (2008) classified Landsat ETM+ derived forest/non-forest maps of Arasba-
is used on both the source and the target domain. This is analogous to our formulation as the classifier network is shared across the domains in our framework. They use a standard PAC-learning formalism. Accordingly, the hypothesis class is the set of all model h w (·) that are parameterized by θ and the goal is to select the best model from the hypothesis class. For any member of this hypothesis class, we denote the true risk on the source domain by e S and the true risk on the target domain with e T . Analogously, µ ˆ S = N 1 ∑ N n=1 δ ( x s n ) denote the
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We seek to learn multidimensional representa- tions of words. Our HMM-based model is able to categorize words in one dimension, by assigning a single HMM latent state to each word. Since the HMM is trained on unlabeled data, this di- mension may partially reflect POS categories, but more likely represents a mixture of many different word dimensions. By adding in multiple hidden layers to our sequence model, we aim to learn a multi-dimensional representation that may help us to capture word features from multiple perspec- tives. The supervised CRF system can then sort out which dimensions are relevant to the sequence- labeling task at hand.
Other work in WSD with applications in Spanish is the work of Montoyo et al.  where the task of WSD consists in assigning the correct sense to words using an electronic dictionary as the source of word definitions. They present a knowledge-based method and a corpus-based method. In the knowledge- based method the underlying hypothesis is that the higher the similarity between two words, the larger the amount of information shared by two of their concepts. The corpus-based method is based on con- ditional maximum-entropy models, it was implemented using a supervised learning method that consists of building word-sense classifiers using a semantically annotated corpus. Among the features for the classifier they used word forms, words in a window, part-of-speech tags and grammatical dependencies.
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SVM is a supervised learning classifier that seeks an optimal hyper-plane to separate two or more classes of samples from the dataset. The mapping the input data into a higher dimensional space is done by using kernel functions with the aim of obtaining a better distribution of the data in the form of three kernels rbf, linear and distributed. Then, an optimal separating hyper-plane will be drawn in the high-dimensional feature space can be easily found in the diagram shown below. In classification stage we are measuring the TPR (True Positive Rate) and FPR (False Positive Rate) with
RF classifier can be described as the collection of tree-structured classifiers..It is an advanced version of bagging  such that randomness is added to it. Instead of splitting each node using the best split among all variables, RF splits each node using the best among a subset of predictors randomly chosen at that node. A new training data set is created from the original data set with replacement. Then, a tree is grown using random feature selection. Grown trees are not pruned . This strategy makes RF unexcelled in accuracy  when compared to other existing algorithms including discriminant analysis, support vector machines and neural networks  . RF is also very fast, it is robust against over fitting, and it is possible to form as many trees as the user wants needs . Two parameters must be defined by user to initialize RF algorithm. These parameters are N and m, which are the number of trees to grow and the number of variables used to split each node, respectively. First, N bootstrap samples are drawn from the 2/3 of the training data set. Remaining 1/3 of the training data, also called out-of-bag (OOB) data, are used to test the error of the predictions. Then, an un-pruned tree from each bootstrap sample is grown such that at each node m predictors are randomly selected as a subset of predictor variables, and the best split from among those variables is chosen. It is crucial to select the number of variables that provides sufficiently low correlation with adequate predictive power . Breiman suggests that setting number of variables (m) equal to the square root of M (number of overall variable) gives generally near optimum results. RF uses Classification and Regression Tree (CART) algorithm to create the trees . At each node, split is performed according to a criterion (e.g. GINI index) in CART algorithm. In this study, GINI index is utilized to perform the split. The GINI index measures class homogeneity and can be written as the equation below :
Abstract— Question Classification is the core component of the Question Answering System. The quality of the question answering system depends on the results of the question classification. Almost all the question classification algorithms are based on the classes defined by Li and Roth .In this paper, a question classification algorithm based on Naïve Bayes Classifier and question semantic similarity is proposed. This paper mainly focuses on Numeric and Location type questions. Naive Bayes Classifier is adopted to classify the questions into Numeric and Location classes and semantic similarity is used to classify the questions into their fine-grained classes. According to Li and Roth, the coarse grained class Numeric and Location has fine-grained class Other. In this paper, we also present the method to replace the Other class in Numeric and Location classes by creating new classes and adding the newly created classes in the hierarchy.