A bibliography of the intersection of genetic search and artificial
... search techniques for the design of ANNs and in operating the neuro-evolution electronic mailing list (more information on neuro-evolution below). The bibliography contains no references which I feel relate solely to ANNs or GAs (genetic algorithms). I am making this bibliography available as an aid ...
... search techniques for the design of ANNs and in operating the neuro-evolution electronic mailing list (more information on neuro-evolution below). The bibliography contains no references which I feel relate solely to ANNs or GAs (genetic algorithms). I am making this bibliography available as an aid ...
Mechanism for propagation of rate signals through a 10
... developed in deeper layers. The development of synchrony is mainly due to the feedforward connections between neighboring layers. The larger the connection probability, the more rapidly the synchrony is developed. The output rate changes monotonically over a wide range of input rate; that is, synchr ...
... developed in deeper layers. The development of synchrony is mainly due to the feedforward connections between neighboring layers. The larger the connection probability, the more rapidly the synchrony is developed. The output rate changes monotonically over a wide range of input rate; that is, synchr ...
A Well-Behaved Algorithm for Simulating Dependence Structures of
... Although simulation of BN structures for experimental study has been widely used, algorithms used for random generation of DAGs are rarely published (e.g., [1, 6]). We review two published algorithms here: Spirtes et al. [9] used a simple algorithm to simulate BNs for testing their learning algorith ...
... Although simulation of BN structures for experimental study has been widely used, algorithms used for random generation of DAGs are rarely published (e.g., [1, 6]). We review two published algorithms here: Spirtes et al. [9] used a simple algorithm to simulate BNs for testing their learning algorith ...
Computational Intelligence: Neural Networks and
... computing that exhibits an ability to learn and/or to deal with new situations, such that the system is perceived to possess intelligent behavior such as generalization, discovery, association and abstraction. CI is a wide concept that can be applied to a large number of fields, including but not li ...
... computing that exhibits an ability to learn and/or to deal with new situations, such that the system is perceived to possess intelligent behavior such as generalization, discovery, association and abstraction. CI is a wide concept that can be applied to a large number of fields, including but not li ...
Current and Future Trends in Feature Selection and Extraction for
... Newton-related methods. Yu et al’s article in this special issue discusses this with respect to non-singular pre-processing techniques (see Ref.15). Decision tree induction methods (see Ref. 11) are also among the most popular learning algorithms. While none of the articles in this issue specifical ...
... Newton-related methods. Yu et al’s article in this special issue discusses this with respect to non-singular pre-processing techniques (see Ref.15). Decision tree induction methods (see Ref. 11) are also among the most popular learning algorithms. While none of the articles in this issue specifical ...
Abstract
... samples belonging to other classes are similar to the distances to the training samples in other classes”. The distances are used to estimate the width of RBF units since they generally imply the range of RBF units. “The value of the function depends only upon the distance from its centre and thus i ...
... samples belonging to other classes are similar to the distances to the training samples in other classes”. The distances are used to estimate the width of RBF units since they generally imply the range of RBF units. “The value of the function depends only upon the distance from its centre and thus i ...
Behavioral learning for adaptive software agents
... reminder” be consistent with this newly acquired domain knowledge. The knowledge-base update is made permanent after the proper evaluation (via more conversation with human and/or the success of plans that are generated by this new knowledge). Next time the “send reminder to colloquium organizer” ac ...
... reminder” be consistent with this newly acquired domain knowledge. The knowledge-base update is made permanent after the proper evaluation (via more conversation with human and/or the success of plans that are generated by this new knowledge). Next time the “send reminder to colloquium organizer” ac ...
Gluck_OutlinePPT_Ch06
... exposure to the behavior-producing stimulus. In spontaneous recovery, a behavior may reappear at original level if stimulus is presented again after a delay. Behavior decreased through habituation can also be renewed (dishabituated) by a novel stimulus. Habituation is stimulus-specific. ...
... exposure to the behavior-producing stimulus. In spontaneous recovery, a behavior may reappear at original level if stimulus is presented again after a delay. Behavior decreased through habituation can also be renewed (dishabituated) by a novel stimulus. Habituation is stimulus-specific. ...
A neural model of hierarchical reinforcement learning
... the different levels, neuroanatomically these are all based in the same basal ganglia. The different selection networks in each layer correspond to different corticostriatal loops, which have been shown to be organized in a hierarchical manner (Frank & Badre, 2012). In addition, the label for the st ...
... the different levels, neuroanatomically these are all based in the same basal ganglia. The different selection networks in each layer correspond to different corticostriatal loops, which have been shown to be organized in a hierarchical manner (Frank & Badre, 2012). In addition, the label for the st ...
Weighted Random Walks for Meta
... based on paths through the network. For example, the extended user − movie − actor − movie − user meta-path enables the system to start with a given user and find other users that have watched movies containing actors in common with the user’s movies. The semantics of this operation of meta-path exp ...
... based on paths through the network. For example, the extended user − movie − actor − movie − user meta-path enables the system to start with a given user and find other users that have watched movies containing actors in common with the user’s movies. The semantics of this operation of meta-path exp ...
LEARNING FROM OBSERVATION: Introduction Observing a task
... who supplies the input-output training instances. The learning system adapts its parameters by some algorithms to generate the desired output patterns from a given input pattern. In absence of trainers, the desired output for a given input instance is not known, and consequently the learner has to ...
... who supplies the input-output training instances. The learning system adapts its parameters by some algorithms to generate the desired output patterns from a given input pattern. In absence of trainers, the desired output for a given input instance is not known, and consequently the learner has to ...
PDF - JMLR Workshop and Conference Proceedings
... Except for the most trivial of cases, vt has no internal structure. In particular, assume that the state space is factored, that is composed of joint assignments to state variables. Even if v is completely independent, vt no longer has any structure (unless Q also represents a completely independent ...
... Except for the most trivial of cases, vt has no internal structure. In particular, assume that the state space is factored, that is composed of joint assignments to state variables. Even if v is completely independent, vt no longer has any structure (unless Q also represents a completely independent ...
Dr. Abeer Mahmoud - PNU-CS-AI
... network how to fulfill a certain task. Teacher presents a number of inputs and their corresponding outputs to the ANN, See how closely the actual outputs match the desired ones. Modify the parameters to better approximate the desired outputs. ANN weights adjusted according to error. Dr.Abeer Mahmoud ...
... network how to fulfill a certain task. Teacher presents a number of inputs and their corresponding outputs to the ANN, See how closely the actual outputs match the desired ones. Modify the parameters to better approximate the desired outputs. ANN weights adjusted according to error. Dr.Abeer Mahmoud ...
Medical Image Segmentation Using Artificial Neural Networks
... abdomen and a set of nuclear scintigraphic images of lung, are used for testing the effectiveness of PCNN. They also showed that PCNN did a good job for contrast enhancement. In addition, they found that PCNN was unable to perform a segmentation ...
... abdomen and a set of nuclear scintigraphic images of lung, are used for testing the effectiveness of PCNN. They also showed that PCNN did a good job for contrast enhancement. In addition, they found that PCNN was unable to perform a segmentation ...
The Wavelet AI Receiver - Northumbria University
... • Both approximations and details can be decimated further. • Need to consider effective contributions from further decomposition. ...
... • Both approximations and details can be decimated further. • Need to consider effective contributions from further decomposition. ...
modeling dynamical systems by means of dynamic bayesian networks
... The world around us is dynamic. Most of the physiological processes occurring in the human body, like many natural phenomena or processes are of dynamical character. Consequently, the modeled phenomena very often are generate time series data. Every variable is observed in successive moments of time ...
... The world around us is dynamic. Most of the physiological processes occurring in the human body, like many natural phenomena or processes are of dynamical character. Consequently, the modeled phenomena very often are generate time series data. Every variable is observed in successive moments of time ...
ANN - Loughborough University Institutional Repository
... An artificial neural network (ANN) is a computational tool composed of simple elements operating in parallel (Demuth et al., 2008) commonly known as neurons that can simulate the working of the human brain and the nervous system learning to perform functions (an input/output map). The neurons are gr ...
... An artificial neural network (ANN) is a computational tool composed of simple elements operating in parallel (Demuth et al., 2008) commonly known as neurons that can simulate the working of the human brain and the nervous system learning to perform functions (an input/output map). The neurons are gr ...
Slides - NYU Computation and Cognition Lab
... Building a model of the regularities in environment (i.e., an internal code that captures aspects of the statistics in the world) also captures the prior structure Learning should largely be about deviation from expectations “One can regard the model or map as something automatically help up for com ...
... Building a model of the regularities in environment (i.e., an internal code that captures aspects of the statistics in the world) also captures the prior structure Learning should largely be about deviation from expectations “One can regard the model or map as something automatically help up for com ...
Temporal Pattern Classification using Spiking Neural Networks
... length. Another problem is that the signal should be buffered before it can be processed by the recognition system. In case of processing of audio-signals this only results in a delay of the output. When this method is used for video-recognition, the extra problem of memory-storage becomes significa ...
... length. Another problem is that the signal should be buffered before it can be processed by the recognition system. In case of processing of audio-signals this only results in a delay of the output. When this method is used for video-recognition, the extra problem of memory-storage becomes significa ...
Neural Net Training for Tic-Tac-Toe
... The methods I explore in finding the most apt Neural Net capable of learning the rules of Tic-TacToe include both adaptive and evolutionary learning strategies. Adaptive learning explores the use of Backpropogation to train the Neural Net on input given a target value. When a legal move is made I se ...
... The methods I explore in finding the most apt Neural Net capable of learning the rules of Tic-TacToe include both adaptive and evolutionary learning strategies. Adaptive learning explores the use of Backpropogation to train the Neural Net on input given a target value. When a legal move is made I se ...
PDF file
... three areas, the sensory area X, the internal area Y and the motor area Z, with an example in Fig. 1(b). The internal neurons in Y have connection with both the sensory end X and the motor end Z. The largest scale account of neural anatomy so far seems the work of visual and motor systems by Fellema ...
... three areas, the sensory area X, the internal area Y and the motor area Z, with an example in Fig. 1(b). The internal neurons in Y have connection with both the sensory end X and the motor end Z. The largest scale account of neural anatomy so far seems the work of visual and motor systems by Fellema ...
Learning pattern recognition and decision making in the insect brain
... Temporal dynamics in the Antennal Lobe The antennal lobe receives the input from the olfactory receptor cells that deliver the information into particular sets of glomeruli. The neural network in the AL is made of projection neurons (PNs), which are excitatory, and lateral neurons (LNs), which are m ...
... Temporal dynamics in the Antennal Lobe The antennal lobe receives the input from the olfactory receptor cells that deliver the information into particular sets of glomeruli. The neural network in the AL is made of projection neurons (PNs), which are excitatory, and lateral neurons (LNs), which are m ...
chapter one
... Applications of this type include the "intelligence" behind robotic movements. This "intelligence" processes inputs and then creates outputs which actually cause some device to move. That movement can span an infinite number of very precise motions. These networks do indeed want to smooth their inpu ...
... Applications of this type include the "intelligence" behind robotic movements. This "intelligence" processes inputs and then creates outputs which actually cause some device to move. That movement can span an infinite number of very precise motions. These networks do indeed want to smooth their inpu ...
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.