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Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
We propose a new regularization method that minimizes the discrepancy between domain-specific latent feature representations directly in the hidden activation space. Expand
FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models
  • E. Lughofer
  • Mathematics, Computer Science
  • IEEE Transactions on Fuzzy Systems
  • 1 December 2008
In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi-Sugeno fuzzy systems proposed by Wang and Mendel. Expand
PANFIS: A Novel Incremental Learning Machine
Learning in nonstationary environment entails a system owning high degree of flexibility capable of assembling its rule base autonomously according to the degree of nonlinearity contained in the system. Expand
Generalized smart evolving fuzzy systems
We propose a new methodology for learning evolving fuzzy systems from data streams in terms of on-line regression/system identification problems. Expand
Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications
  • E. Lughofer
  • Computer Science
  • Studies in Fuzziness and Soft Computing
  • 21 January 2011
I. Introduction.- Part I - Basic Methodologies.- II. Basic Algorithms for EFS.- III. EFS Approaches for Regression and Classification.- Part II - Advanced Concepts.- IV. Expand
GENEFIS: Toward an Effective Localist Network
Generic Evolving Neuro-Fuzzy Inference System (GENEFIS) is proposed in this paper. Expand
SparseFIS: Data-Driven Learning of Fuzzy Systems With Sparsity Constraints
In this paper, we deal with a novel data-driven learning method [sparse fuzzy inference systems (SparseFIS)] for Takagi-Sugeno (T-S) fuzzy systems, extended by including rule weights. Expand
Evolving fuzzy classifiers using different model architectures
In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called eClass and FLEXFIS-Class. Expand
On-line elimination of local redundancies in evolving fuzzy systems
In this paper, we examine approaches for reducing the complexity of evolving fuzzy systems (EFSs) by eliminating local redundancies during training, evolving the models on on-line data streams. Expand
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
In this paper, we present new approaches to handling drift and shift in on-line data streams with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their structure (rule base and parameters) is not fixed and not pre-determined, but is extracted from data streams on- line and in an incremental manner. Expand