The following pages link to Machine Learning: ECML 2004 (Q5450726):
Displaying 37 items.
- Online passive-aggressive active learning (Q298271) (← links)
- Dynamic class imbalance learning for incremental LPSVM (Q459443) (← links)
- Learning SVM with weighted maximum margin criterion for classification of imbalanced data (Q649635) (← links)
- An efficient weighted Lagrangian twin support vector machine for imbalanced data classification (Q736317) (← links)
- SVM classification for imbalanced data sets using a multiobjective optimization framework (Q744712) (← links)
- Classification using proximity catch digraphs (Q782440) (← links)
- Cost-sensitive boosting for classification of imbalanced data (Q996413) (← links)
- Modified neural network algorithms for predicting trading signals of stock market indices (Q1040018) (← links)
- Manifold-based synthetic oversampling with manifold conformance estimation (Q1640402) (← links)
- Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs (Q1669160) (← links)
- Improving SVM classification on imbalanced datasets by introducing a new bias (Q1695096) (← links)
- Distance-based margin support vector machine for classification (Q1733409) (← links)
- Imbalanced classification in sparse and large behaviour datasets (Q1741360) (← links)
- Large-scale linear nonparallel SVMs (Q1797804) (← links)
- Combining experts in order to identify binding sites in yeast and mouse genomic data (Q1932034) (← links)
- Large-scale distributed sparse class-imbalance learning (Q2198074) (← links)
- Using pre \& post-processing methods to improve binding site predictions (Q2270825) (← links)
- A dynamic over-sampling procedure based on sensitivity for multi-class problems (Q2275973) (← links)
- Training and assessing classification rules with imbalanced data (Q2435707) (← links)
- Densifying distance spaces for shape and image retrieval (Q2513322) (← links)
- Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors (Q2514835) (← links)
- Imbalanced data classification using second-order cone programming support vector machines (Q2629845) (← links)
- Using self-organizing maps for binary classification with highly imbalanced datasets (Q2815668) (← links)
- Weighted Support Vector Machine Using<i>k</i>-Means Clustering (Q2876146) (← links)
- A study on imbalance support vector machine algorithms for sufficient dimension reduction (Q2979033) (← links)
- A fuzzy support vector machine algorithm for imbalanced data classification (Q2984049) (← links)
- An Improved Algorithm for SVMs Classification of Imbalanced Data Sets (Q3405715) (← links)
- An Improved Algorithm of Unbalanced Data SVM (Q4930942) (← links)
- Adaptive kernel scaling support vector machine with application to a prostate cancer image study (Q5073413) (← links)
- A comparative study of the use of large margin classifiers on seismic data (Q5130137) (← links)
- Proximal support vector machine techniques on medical prediction outcome (Q5138554) (← links)
- An Empirical Overview of the No Free Lunch Theorem and Its Effect on Real-World Machine Learning Classification (Q5380387) (← links)
- Structural, Syntactic, and Statistical Pattern Recognition (Q5466429) (← links)
- A new non-kernel quadratic surface approach for imbalanced data classification in online credit scoring (Q6064472) (← links)
- Prediction of forest fire risk for artillery military training using weighted support vector machine for imbalanced data (Q6546757) (← links)
- Trade-off between bagging and boosting for quantum separability-entanglement classification (Q6588838) (← links)
- Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection (Q6667804) (← links)
