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Learning to Solve Arithmetic Word Problems with Verb Categorization
TLDR
The paper analyzes the arithmetic-word problems “genre”, identifying seven categories of verbs used in such problems, and reports the first learning results on this task without reliance on predefined templates and makes the data publicly available.
A data mining approach for diagnosis of coronary artery disease
An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams
TLDR
An ensemble algorithm to classify instances of non-stationary data streams in a semi-supervised environment that uses unlabeled instances as well as labeled ones in the learning task to recognize recurring concept drifts of data streams.
Learning Typed Entailment Graphs with Global Soft Constraints
TLDR
This paper presents a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph, and shows large improvements over local similarity scores on two entailment data sets.
Diagnosis of Coronary Artery Disease Using Cost-Sensitive Algorithms
TLDR
10-fold cross validation on cost-sensitive algorithms along with base classifiers of Naïve Bayes, Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and C4.5 were employed and the SMO algorithm has yield to very high sensitivity and accuracy rates, the likes of which have not been reported simultaneously in the existing literature.
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
TLDR
GRAB reveals the underlying network structure substantially better than four state-of-the-art competitors on synthetic data and outperforms its competitors in revealing known functional gene sets and potentially novel genes that drive cancer.
Using a classifier pool in accuracy based tracking of recurring concepts in data stream classification
TLDR
This paper proposes a learning algorithm called Pool and Accuracy based Stream Classification with some variations, which takes the advantage of maintaining a pool of classifiers to track recurring concepts and shows the effectiveness of weighted classifiers method while dealing with sudden concept drifting datasets.
New Management Operations on Classifiers Pool to Track Recurring Concepts
TLDR
This paper presents a novel algorithm to manage the pool that holds an ensemble of classifiers for each concept and shows the performance dominance of using this method to the most promising stream classification algorithms.
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