[PDF] Top 20 Training Highly Multiclass Classifiers
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Training Highly Multiclass Classifiers
... of highly-confusable ...candidate classifiers, each of which made 100 errors, but one made all 100 errors between dolphin and porpoise and the other made 100 more random ...these classifiers equal, ... See full document
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A Multi-class SVM Classifier Utilizing Binary Decision Tree
... Machine classifiers utilizing binary decision tree (SVM-BDT) for solving multiclass problems was ...of training and testing are improved, while keeping comparable or offering better recognition rates ... See full document
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A Theory of Multiclass Boosting
... The game-theoretic strategies are non-adaptive in that they presume prior knowledge about the edge, that is, how much better than random are the weak classifiers. Algorithms that are adaptive, such as AdaBoost, ... See full document
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Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers
... Discriminative models for the multiclass and binary classifica- tion problem are obtained by using standard learning algorithms. The proposed method is not limited to a specific classification learning method. For ... See full document
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Learning When Concepts Abound
... a training instance that belongs to class c does not imply that the feature will point to class c in the index ...linear) classifiers, but the issues of space and time efficient learning remain, and ... See full document
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Training genetic programming classifiers by vicinal-risk minimization
... between training er- ror and a measure of model complexity (node ...perform highly- effective bloat control almost as a side-effect of minimizing tree ... See full document
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Breaking the Closed World Assumption in Text Classification
... One-class classifiers, which only rely on posi- tive training data, are natural starting solutions to the multiclass open classification ...negative training data is used, one-class ... See full document
9
Classification of Event Related Google Alerts Using Machine Learning
... the training dataset into k equal size subsets ...a highly skewed class distribution generally predicts the more frequently occurring classes much more often than the infrequently occurring ...most ... See full document
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A distributed block coordinate descent method for training l1 regularized linear classifiers
... Now consider GROCK , FPA and HYDRA which are based on using Lipschitz con- stants (L j ). We found GROCK to be either unstable and diverging or extremely slow. The left side plot in figure 2 depicts these behaviors. The ... See full document
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Multiclass Boosting for Weak Classifiers
... in our experiments find an attribute a and a value v which are used to divide the training set into two subsets. If attribute a is continuous and its value on x is at most v then x belongs to the first subset; ... See full document
22
Opinion of Tweets Using Sentimental Analysis
... The Emotion data set was created by Go, Bhayani, and Huang for a project at Stanford University by collecting tweets with positive ‘+’ and negative ‘-‘emotions. Messages containing both positive and negative emotions ... See full document
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Classifying Unstructured Text Using Structured Training Instances and an Ensemble of Classifiers
... Since there are misclassified values there is room for improvement. Potentially, this can be achieved with better weight tuning and more heuristic procedures during the combination. Methodologies that use the confi- ... See full document
16
Opinion Mining of Spanish Customer Comments with Non Expert Annotations on Mechanical Turk
... of training several polarity classifiers on six different training sets, each of them generated from the MTurk anno- tations of ...different training sets are: (i) the original dataset of 1000 ... See full document
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Composite Multiclass Losses
... for multiclass prediction has received recent attention (Zhang, 2004; Hill and Doucet, 2007; Tewari and Bartlett, 2007; Liu, 2007; Santos-Rodr´ıguez et ...a multiclass generalisation of “admissible losses” ... See full document
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Alignment-free classification of COI DNA barcode data with the Python package Alfie
... able training data may be scant, providing a limitation in training of DNNs or other machine learning models which rely on abundant training ... See full document
9
Active learning for detection of stance components
... In contrast, an F-score of 0.5 for detecting positive and negative sentiment is far from the results achieved in previous studies on the same classification task (Socher et al., 2013). A manual annotation of 11,855 ... See full document
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Delta training: Simple Semi Supervised Text Classification using Pretrained Word Embeddings
... of classifiers as a form of model en- semble, and then initializes their word embed- dings differently: one using random, the other using pretrained word ...Delta- training, outperforms the ... See full document
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Breaking the Curse of Kernelization: Budgeted Stochastic Gradient Descent for Large-Scale SVM Training
... (SVM) training due to its simplicity and ...SVM training which have constant space and constant time complexity per ...during training through several budget maintenance ...during training and ... See full document
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Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
... complexity classifiers may not be able to achieve good accuracy in difficult classification tasks such as speech recognition or image recognition (Cortes et ...base classifiers— such as very deep decision ... See full document
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Volume 3, Issue 3, March 2014 Page 360
... The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multi set) of ... See full document
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