Abstract Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of labels. While theory, any is sufficient, practice, this has substantial impact on quality final prediction. Dynamic classifier denote idea that each instance to classify, which labels predicted dynamically chosen. The complexi...

Multi-label classification has attracted much attention in the machine learning community to address problem of assigning single samples more than one (not necessarily non-overlapping) class at same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which is able self-adapt and self-evolve its structure with new incoming incremental, single-pass manner. It based on a multi-outpu...

Autoencoders are data-specific compression algorithms learned automatically from examples. The predominant approach has been to construct single large global models that cover the domain. However, training and evaluating of increasing size comes at price additional time computational cost. Conditional computation, sparsity, model pruning techniques can reduce these costs while maintaining perfo...

Quantum machine learning has the potential to provide powerful algorithms for artificial intelligence. The pursuit of quantum advantage in is an active area research. For current noisy, intermediate-scale (NISQ) computers, various quantum-classical hybrid have been proposed. One such previously proposed algorithm a gate-based variational embedding classifier, which composed classical neural net...