Unbalanced Decision Trees for Multi-class
... proceed until a leaf node is reached. In contrast, we can say that UDT uses a “knock-out” strategy with at most (k-1) classifiers to make a decision on any input pattern and is an example of ‘vine’ structured testing strategy [12]. It will be a more challenging problem when k becomes very large. We ...
... proceed until a leaf node is reached. In contrast, we can say that UDT uses a “knock-out” strategy with at most (k-1) classifiers to make a decision on any input pattern and is an example of ‘vine’ structured testing strategy [12]. It will be a more challenging problem when k becomes very large. We ...
neural spike
... When no stimulation is present, there is a spontaneous activation of polychronous groups. If the size of the network exceeds certain threshold, a random activation of a few groups corresponding to a previously seen stimulus may activate other groups corresponding to the same stimulus so that the tot ...
... When no stimulation is present, there is a spontaneous activation of polychronous groups. If the size of the network exceeds certain threshold, a random activation of a few groups corresponding to a previously seen stimulus may activate other groups corresponding to the same stimulus so that the tot ...
Cortical region interactions and the functional role of apical
... Keywords: Cerebral cortex, Pyramidal cells, Dendrites, Neural Networks, Attention, Learning, Memory, ...
... Keywords: Cerebral cortex, Pyramidal cells, Dendrites, Neural Networks, Attention, Learning, Memory, ...
PPT
... for building “intelligent” machines? • Symbolic AI is well-suited for representing explicit knowledge that can be appropriately formalized. • However, learning in biological systems is mostly implicit – it is an adaptation process based on uncertain information and reasoning. • ANNs are inherently p ...
... for building “intelligent” machines? • Symbolic AI is well-suited for representing explicit knowledge that can be appropriately formalized. • However, learning in biological systems is mostly implicit – it is an adaptation process based on uncertain information and reasoning. • ANNs are inherently p ...
Using Pattern Recognition in Network Intrusion Detectors
... nodes arranged in layers and connected together to form a network. Our first test was with Feed Forward Neural Networks (FFNN). In general, a FFNN consists of a layer of input nodes, a layer (or multiple layers) of hidden neurons and a layer of output neurons. Neural networks are trained using data ...
... nodes arranged in layers and connected together to form a network. Our first test was with Feed Forward Neural Networks (FFNN). In general, a FFNN consists of a layer of input nodes, a layer (or multiple layers) of hidden neurons and a layer of output neurons. Neural networks are trained using data ...
ICAISC 2004 Preliminary Program
... Smoking is prohibited at all conference events. Your conference badge is your admission to all events and sessions. The importance of the papers is not related to the form of the presentation. Overhead and computer projectors will be available on all oral sessions. Posters should be prepared with th ...
... Smoking is prohibited at all conference events. Your conference badge is your admission to all events and sessions. The importance of the papers is not related to the form of the presentation. Overhead and computer projectors will be available on all oral sessions. Posters should be prepared with th ...
IOSR Journal of Computer Engineering (IOSR-JCE)
... systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network. In a neural network for handwriting recognition, a set of ...
... systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected "neurons" that can compute values from inputs by feeding information through the network. In a neural network for handwriting recognition, a set of ...
9.4 Why do we want machine learning
... question that the ability to adapt to new surroundings and to solve new problems is an important characteristic of intelligent entities. Can we expect such abilities in programs? Ada Augusta, one of the earliest philosophers of computing, wrote: "The Analytical Engine has no pretensions whatever to ...
... question that the ability to adapt to new surroundings and to solve new problems is an important characteristic of intelligent entities. Can we expect such abilities in programs? Ada Augusta, one of the earliest philosophers of computing, wrote: "The Analytical Engine has no pretensions whatever to ...
1.0 0.0 1.0 2.0 3.0 0.0 0.4 0.8 Time (sec)
... Meanwhile, the kinematic data of CMJ were recorded and digitized with a Peak Performance System at 120Hz. The GRF and kinematic data of support phase were than normalized as 100%. To calculate the joint torque at ankle, knee and hip, 2D inverse dynamics model was developed by inputting the GRF, kine ...
... Meanwhile, the kinematic data of CMJ were recorded and digitized with a Peak Performance System at 120Hz. The GRF and kinematic data of support phase were than normalized as 100%. To calculate the joint torque at ankle, knee and hip, 2D inverse dynamics model was developed by inputting the GRF, kine ...
State-Dependent Computation Using Coupled Recurrent Networks
... activity depends on ordered neighbor connections. The positive feedback effected by the excitory neighbor connections enhances the features of the input that match patterns embedded in the excitatory synaptic weights. The overall strength of the excitatory response is used to suppress outliers via t ...
... activity depends on ordered neighbor connections. The positive feedback effected by the excitory neighbor connections enhances the features of the input that match patterns embedded in the excitatory synaptic weights. The overall strength of the excitatory response is used to suppress outliers via t ...
McCulloch-Pitts Neuron
... to perform the AND function. Train a McCulloch-Pitts neural network to perform the AND NOT function. Train a McCulloch-Pitts neural network to perform the XOR function. The McCulloch-Pitts Neuron ...
... to perform the AND function. Train a McCulloch-Pitts neural network to perform the AND NOT function. Train a McCulloch-Pitts neural network to perform the XOR function. The McCulloch-Pitts Neuron ...
Computational physics: Neural networks
... possible, which is the stochastic binary neuron. The pioneering work in this direction was done by McCulloch and Pitts [1] in the ’40s. Taking the thresholding property of neurons to the extreme, they proposed that neurons perform logical operations on their inputs, such as AND and OR. One can show ...
... possible, which is the stochastic binary neuron. The pioneering work in this direction was done by McCulloch and Pitts [1] in the ’40s. Taking the thresholding property of neurons to the extreme, they proposed that neurons perform logical operations on their inputs, such as AND and OR. One can show ...
Robust Reinforcement Learning Control with Static and Dynamic
... with uncertainties and candidate controllers to analyze the stability of the true system. This is a significant advance in practical control, but designing a controller that remains stable in the presence of uncertainties limits the aggressiveness of the resulting controller, resulting in suboptimal ...
... with uncertainties and candidate controllers to analyze the stability of the true system. This is a significant advance in practical control, but designing a controller that remains stable in the presence of uncertainties limits the aggressiveness of the resulting controller, resulting in suboptimal ...
Learning Sum-Product Networks with Direct and Indirect Variable
... different from most previous work with SPNs, where sum nodes represent mixtures of distributions and are not deterministic. SPNs can be made deterministic by explicitly defining random variables to represent the mixtures, making the different children of each sum node deterministically associated wi ...
... different from most previous work with SPNs, where sum nodes represent mixtures of distributions and are not deterministic. SPNs can be made deterministic by explicitly defining random variables to represent the mixtures, making the different children of each sum node deterministically associated wi ...
Catastrophic interference
Catastrophic Interference, also known as catastrophic forgetting, is the tendency of a artificial neural network to completely and abruptly forget previously learned information upon learning new information. Neural networks are an important part of the network approach and connectionist approach to cognitive science. These networks use computer simulations to try and model human behaviours, such as memory and learning. Catastrophic interference is an important issue to consider when creating connectionist models of memory. It was originally brought to the attention of the scientific community by research from McCloskey and Cohen (1989), and Ractcliff (1990). It is a radical manifestation of the ‘sensitivity-stability’ dilemma or the ‘stability-plasticity’ dilemma. Specifically, these problems refer to the issue of being able to make an artificial neural network that is sensitive to, but not disrupted by, new information. Lookup tables and connectionist networks lie on the opposite sides of the stability plasticity spectrum. The former remains completely stable in the presence of new information but lacks the ability to generalize, i.e. infer general principles, from new inputs. On the other hand, connectionst networks like the standard backpropagation network are very sensitive to new information and can generalize on new inputs. Backpropagation models can be considered good models of human memory insofar as they mirror the human ability to generalize but these networks often exhibit less stability than human memory. Notably, these backpropagation networks are susceptible to catastrophic interference. This is considered an issue when attempting to model human memory because, unlike these networks, humans typically do not show catastrophic forgetting. Thus, the issue of catastrophic interference must be eradicated from these backpropagation models in order to enhance the plausibility as models of human memory.