Intrusion Detection Using Data Mining Along Fuzzy Logic and
... to handle imprecision and vagueness as well as mature inference mechanisms using varying degrees of truth. Since boundaries are not always clearly defined, fuzzy logic can be used to identify complex pattern or behavior variations [3]. This is accomplished by building an Intrusion Detection System t ...
... to handle imprecision and vagueness as well as mature inference mechanisms using varying degrees of truth. Since boundaries are not always clearly defined, fuzzy logic can be used to identify complex pattern or behavior variations [3]. This is accomplished by building an Intrusion Detection System t ...
A plastic axonal hotspot
... such mechanisms. The two studies also open the door to exploring how AIS plasticity occurs in different types of neuron under various conditions that affect neural circuits. For instance, both teams used relatively crude methods to alter neuronal activity: long-term increases in neuronal activity or ...
... such mechanisms. The two studies also open the door to exploring how AIS plasticity occurs in different types of neuron under various conditions that affect neural circuits. For instance, both teams used relatively crude methods to alter neuronal activity: long-term increases in neuronal activity or ...
Proceedings of 2013 BMI the Second International Conference on
... Recently, resting state-fMRI (rs-fMRI) has emerged as an effective way to investigate brain networks. In this technique, fMRI data is acquired when an individual is asked to do nothing but stay awake while lying in the MRI scanner. The rs-fMRI technique emerged from the phenomena that approximately ...
... Recently, resting state-fMRI (rs-fMRI) has emerged as an effective way to investigate brain networks. In this technique, fMRI data is acquired when an individual is asked to do nothing but stay awake while lying in the MRI scanner. The rs-fMRI technique emerged from the phenomena that approximately ...
Determining the number of model runs: Treating cognitive models as
... run. ACT-R is an example of a hybrid architecture that has stochastic components. The example model described later in this paper is an ACT-R model (Anderson, 2007). To understand the dilemma presented in the next section a brief description of the ACT-R architecture is required. ACT-R is symbolic ...
... run. ACT-R is an example of a hybrid architecture that has stochastic components. The example model described later in this paper is an ACT-R model (Anderson, 2007). To understand the dilemma presented in the next section a brief description of the ACT-R architecture is required. ACT-R is symbolic ...
Repetition and the brain: neural models of stimulus
... terms of their ability to account for the main properties of this phenomenon as measured with single-cell recordings and neuroimaging techniques. We also discuss future directions for distinguishing between these models, which will be important for understanding the neural consequences of repetition ...
... terms of their ability to account for the main properties of this phenomenon as measured with single-cell recordings and neuroimaging techniques. We also discuss future directions for distinguishing between these models, which will be important for understanding the neural consequences of repetition ...
Repetition and the brain: neural models of stimulus
... terms of their ability to account for the main properties of this phenomenon as measured with single-cell recordings and neuroimaging techniques. We also discuss future directions for distinguishing between these models, which will be important for understanding the neural consequences of repetition ...
... terms of their ability to account for the main properties of this phenomenon as measured with single-cell recordings and neuroimaging techniques. We also discuss future directions for distinguishing between these models, which will be important for understanding the neural consequences of repetition ...
Online Bayesian Passive-Aggressive Learning
... Although the classical regime of online learning is based on decision theory, recently much attention has been paid to the theory and practice of online probabilistic inference in the context of Big Data. Rooted either in variational inference or Monte Carlo sampling methods, there are broadly two l ...
... Although the classical regime of online learning is based on decision theory, recently much attention has been paid to the theory and practice of online probabilistic inference in the context of Big Data. Rooted either in variational inference or Monte Carlo sampling methods, there are broadly two l ...
hierarchical intelligent simulation
... Knowledge hierarchy is not based on a form of simplifying abstraction, as are the other hierarchy types; it explicitly represents metaknowledge (knowledge about knowledge), based on a reflexive abstraction form, that links (abstract) objects to recursive functions defined for these objects. Interlev ...
... Knowledge hierarchy is not based on a form of simplifying abstraction, as are the other hierarchy types; it explicitly represents metaknowledge (knowledge about knowledge), based on a reflexive abstraction form, that links (abstract) objects to recursive functions defined for these objects. Interlev ...
Discontinuity in evolution: how different levels of organization imply
... located at the periphery of the environment. (Os are free to move out of the 20x20 environment even if they can increase their fitness only by remaining in the environment). Os are placed in individual copies in the environment (i.e. they live in isolation) and they do not change during the course o ...
... located at the periphery of the environment. (Os are free to move out of the 20x20 environment even if they can increase their fitness only by remaining in the environment). Os are placed in individual copies in the environment (i.e. they live in isolation) and they do not change during the course o ...
how different levels of organization imply pre
... located at the periphery of the environment. (Os are free to move out of the 20x20 environment even if they can increase their fitness only by remaining in the environment). Os are placed in individual copies in the environment (i.e. they live in isolation) and they do not change during the course o ...
... located at the periphery of the environment. (Os are free to move out of the 20x20 environment even if they can increase their fitness only by remaining in the environment). Os are placed in individual copies in the environment (i.e. they live in isolation) and they do not change during the course o ...
Dropout as a Bayesian Approximation: Representing Model
... deep learning tools. Standard deep learning tools for regression and classification do not capture model uncertainty. In classification, predictive probabilities obtained at the end of the pipeline (the softmax output) are often erroneously interpreted as model confidence. A model can be uncertain i ...
... deep learning tools. Standard deep learning tools for regression and classification do not capture model uncertainty. In classification, predictive probabilities obtained at the end of the pipeline (the softmax output) are often erroneously interpreted as model confidence. A model can be uncertain i ...
