• Publications
  • Influence
A shared task involving multi-label classification of clinical free text
TLDR
A shared task involving the assignment of ICD-9-CM codes to radiology reports resulted in the first freely distributable corpus of fully anonymized clinical text, suggesting that human-like performance on this task is within the reach of currently available technologies.
Survey of Neural Transfer Functions
The choice of transfer functions may strongly influence complexity and performance of neural networks. Although sigmoidal transfer functions are the most common there is no a priori reason why models
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
TLDR
Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables.
Cognitive Architectures: Where do we go from here?
Apparatus for scanning the image on an image-bearing member moved past an array of thin film light sensitive elements, said apparatus relying upon the concept of "proximity focusing" in order to
Comparison of Shannon, Renyi and Tsallis Entropy Used in Decision Trees
TLDR
Modified C4.5 decision trees based on Tsallis and Renyi entropies have been tested on several high-dimensional microarray datasets with interesting results and may be used in any decision tree and information selection algorithm.
What Is Computational Intelligence and Where Is It Going?
  • Wlodzislaw Duch
  • Computer Science
    Challenges for Computational Intelligence
  • 2007
TLDR
CI is defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions.
Artificial intelligence approaches for rational drug design and discovery.
TLDR
An overview of basic concepts and methodology in the fields of machine learning and artificial intelligence (AI) is presented, with an emphasis on methods that enable an intuitive interpretation of the results and facilitate gaining an insight into the structure of the problem at hand.
Computational intelligence methods for rule-based data understanding
TLDR
All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the rule-extraction stage, and tradeoff between rejection and error level at therule optimization stage.
Transfer functions: hidden possibilities for better neural networks
TLDR
Several possibilities of using transfer functions of different types in neural models are discussed, including enhance- ment of input features, selection of functions from a fixed pool, optimization of parameters of general type of functions, regularization of large networks with heterogeneous nodes and constructive approaches.
Transition of the functional brain network related to increasing cognitive demands
TLDR
The results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance, and it was found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions.
...
...