multiclass multiple kernel learning algorithms are proposed instead of a single one, which can not only combine multiple kernels corresponding to different notions of similarity or information from multiple feature subsets, but also avoid kernel parameters selecting and fuse distinctions of multiple ...
The class also provides the “code_size” argument that specifies the size of the encoding for the classes as a multiple of the number of classes, e.g. the number of bits to encode for each class label. For example, if we wanted an encoding with bit strings with a length of 6 bits...
Pouyanfar S, Chen S C, Shyu M L (2018) Deep spatio-temporal representation learning for multi-class imbalanced data classification. In: IEEE international conference on information reuse and integration (IRI). IEEE, pp 386–393 Guo Y, Xiao H (2018) Multiclass multiple kernel learning using ...
method for power transformer based on multiclass multiple-kernel learning support vector machine[J].Proceedings of the CSEE,2010,30(13): 128-134(in ... 郭创新,朱承治,张琳,... - 《中国电机工程学报》 被引量: 112发表: 2010年 加载更多站...
For an example that shows how to produce this output, see Hyperparameter Optimization with Multiple Constraint Bounds. More About collapse all Bag-of-Tokens Model In the bag-of-tokens model, the value of predictor j is the nonnegative number of occurrences of token j in the observation. The...
Create a default linear learner template, and then use it to train an ECOC model containing multiple binary linear classification models. Load the NLP data set. load nlpdata X is a sparse matrix of predictor data, and Y is a categorical vector of class labels. The data contains 13 classes...
Ensemble learning: Ensemble learning is a technique that improves classification performance by combining multiple classifiers. In the case of handling multi-class imbalanced data, ensemble learning methods such as Bagging, Boosting, and Stacking can be employed to integrate the predictions of multiple cl...
7.6 Transforming multiple classes to binary ones Recall from Chapter 6 that some learning algorithms—for example, standard support vector machines—only work with two-class problems. In most cases, sophisticated multiclass variants have been developed, but they may be very slow or difficult to impl...
ClassificationECOC is an error-correcting output codes (ECOC) classifier for multiclass learning, where the classifier consists of multiple binary learners such as support vector machines (SVMs).
Alpaydin Multiple kernel learning algorithms Journal of Machine Learning Research, 12 (2011), pp. 2211-2268 View in ScopusGoogle Scholar Halliday et al., 2008 G. Halliday, M. Hely, W. Reid, J. Morris The progression of pathology in longitudinally followed patients with Parkinson's disease ...