complete file
... parameters of the CEBP network. Furthermore, it is possible to refine a set of rules by inserting them into the CEBP network, training the network and then extracting them. There are also methods for extraction of propositional rules from a trained multilayered perceptron when considered as a black ...
... parameters of the CEBP network. Furthermore, it is possible to refine a set of rules by inserting them into the CEBP network, training the network and then extracting them. There are also methods for extraction of propositional rules from a trained multilayered perceptron when considered as a black ...
Solving the Problem of Negative Synaptic Weights in Cortical Models
... arbitrary transformations on the encoded variables. Conveniently, the same methods can be employed. Instead of finding decoders φ to decode an estimate of x (i.e., computing the identity function), the same linear leastsquares method can be used to provide decoders φ g(x) for some arbitrary function ...
... arbitrary transformations on the encoded variables. Conveniently, the same methods can be employed. Instead of finding decoders φ to decode an estimate of x (i.e., computing the identity function), the same linear leastsquares method can be used to provide decoders φ g(x) for some arbitrary function ...
A Self-Organizing Neural Network That Learns to
... stage of visual processing. (Marshall, 1991) describes evidence that suggests that the same early processing mechanisms maintain a representation of temporarily occluded objects for some amount of time after they have disappeared behind an occluder, and that these representations of invisible object ...
... stage of visual processing. (Marshall, 1991) describes evidence that suggests that the same early processing mechanisms maintain a representation of temporarily occluded objects for some amount of time after they have disappeared behind an occluder, and that these representations of invisible object ...
An introduction to artificial intelligence applications in petroleum
... knowledge base and a set of algorithms or rules that infer new facts from knowledge and from incoming data. An expert system uses the knowledge base of human expertise to provide expert advice and aid in solving problems. The degree of problem solving is based on the quality of the data and rules ob ...
... knowledge base and a set of algorithms or rules that infer new facts from knowledge and from incoming data. An expert system uses the knowledge base of human expertise to provide expert advice and aid in solving problems. The degree of problem solving is based on the quality of the data and rules ob ...
6.034 Neural Net Notes
... and extend the analysis to handle multiple-neurons per layer. Also, I develop the back propagation rule, which is often needed on quizzes. I use a notation that I think improves on previous explanations. The reason is that the notation here plainly associates each input, output, and weight with a re ...
... and extend the analysis to handle multiple-neurons per layer. Also, I develop the back propagation rule, which is often needed on quizzes. I use a notation that I think improves on previous explanations. The reason is that the notation here plainly associates each input, output, and weight with a re ...
A Multistrategy Approach to Classifier Learning from Time
... the time series. This definition is important because it yields a general mathematical characterization for individually weighted “windows” of past values (time delay or resolution) and nonlinear memories that “fade” smoothly (attenuated decay, or depth) (Principé & deVries, 1992; Mozer, 1994; Prin ...
... the time series. This definition is important because it yields a general mathematical characterization for individually weighted “windows” of past values (time delay or resolution) and nonlinear memories that “fade” smoothly (attenuated decay, or depth) (Principé & deVries, 1992; Mozer, 1994; Prin ...
Classification of Clustered Microcalcifications using Resilient
... accepted imaging method for routine breast cancer screening. It is recommended that women at the ages of 40 or above should have a mammogram every one to two years [3]. Although mammography is widely used around the world for breast cancer detection, it is difficult for expert radiologists to provid ...
... accepted imaging method for routine breast cancer screening. It is recommended that women at the ages of 40 or above should have a mammogram every one to two years [3]. Although mammography is widely used around the world for breast cancer detection, it is difficult for expert radiologists to provid ...
Visual Motion Perception using Critical Branching Neural Computation
... and the resulting error signal was used to update connection weights with the delta learning rule (using momentum = 0.5, learning rate = 0.00001). At testing, the maximally active readout unit in each group was compared with the targeted output to assess model accuracy (both X- and Y-coordinate unit ...
... and the resulting error signal was used to update connection weights with the delta learning rule (using momentum = 0.5, learning rate = 0.00001). At testing, the maximally active readout unit in each group was compared with the targeted output to assess model accuracy (both X- and Y-coordinate unit ...
Inhibition
... • Automatic memory retrieval – If there is disagreement between the task at hand and a recent memory, this will take longer because you need to resolve the conflict ...
... • Automatic memory retrieval – If there is disagreement between the task at hand and a recent memory, this will take longer because you need to resolve the conflict ...
Neural Network Applications in Stock Market Predictions
... According to many authors, NN methodology underestimates the design of NN architecture (topology), and methods of training, testing, evaluating, and implementing the network [13]. Since the data regarding the evaluation and implementation phase were not available in all analyzed articles, the paper ...
... According to many authors, NN methodology underestimates the design of NN architecture (topology), and methods of training, testing, evaluating, and implementing the network [13]. Since the data regarding the evaluation and implementation phase were not available in all analyzed articles, the paper ...
Training
... in the statistics of the input distributions: regions in the input space H from which the sample vectors x are drawn with a high probability of occurrence are mapped onto larger domains of the output space A, and therefore with better resolution than regions in H from which sample vectors x are draw ...
... in the statistics of the input distributions: regions in the input space H from which the sample vectors x are drawn with a high probability of occurrence are mapped onto larger domains of the output space A, and therefore with better resolution than regions in H from which sample vectors x are draw ...
Structured Regularizer for Neural Higher
... as lower bound from a mixture of models sharing parts of each other, e.g. neural sub-networks, and relate it to ensemble learning. Furthermore, it can be expressed explicitly as regularization term in the training objective. We exemplify its effectiveness by exploring the introduced NHOLC-CRFs for s ...
... as lower bound from a mixture of models sharing parts of each other, e.g. neural sub-networks, and relate it to ensemble learning. Furthermore, it can be expressed explicitly as regularization term in the training objective. We exemplify its effectiveness by exploring the introduced NHOLC-CRFs for s ...
The Emergence of Selective Attention through - laral
... enhances the activity of the neurons that code for the relevant feature in parallel throughout the visual field, and thus that representations of distracters sharing one or more features with the target are enhanced as well, slowing down the response decision process. Overall, the results of these e ...
... enhances the activity of the neurons that code for the relevant feature in parallel throughout the visual field, and thus that representations of distracters sharing one or more features with the target are enhanced as well, slowing down the response decision process. Overall, the results of these e ...
Challenges of understanding brain function by selective modulation
... Beyond the first stages of sensory processing or the penultimate stages of motor processing, most networks in the brain cannot be approximated by a feedforward structure. Higher brain areas exhibit more recurrency for which it is non-trivial to reveal the specific activity patterns that implement a ...
... Beyond the first stages of sensory processing or the penultimate stages of motor processing, most networks in the brain cannot be approximated by a feedforward structure. Higher brain areas exhibit more recurrency for which it is non-trivial to reveal the specific activity patterns that implement a ...
A Developmental Approach to Intelligence
... science has ever undertaken.” (Kolata 1982). Today, artificial intelligence is an active research area; however, there are few researchers actually pursuing the goal of creating completely autonomous, intelligent systems. AI researchers, cognitive scientists, and philosophers are divided in opinion ...
... science has ever undertaken.” (Kolata 1982). Today, artificial intelligence is an active research area; however, there are few researchers actually pursuing the goal of creating completely autonomous, intelligent systems. AI researchers, cognitive scientists, and philosophers are divided in opinion ...
Fuzzy Logic and Neural Nets
... in, and each set covers a range of values • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all the rules we partially believe, and take their weighted aver ...
... in, and each set covers a range of values • Two options in going from current state to a single value: – Mean of Max: Take the rule we believe most strongly, and take the (weighted) average of its possible values – Center of Mass: Take all the rules we partially believe, and take their weighted aver ...
131-300-1
... one of output classes change to 1, all output classes do not change any more. This function is implemented by means of OR and multiplexer gates. In other words, when one input of OR gate is being 1, second input of multiplexers are selected. Thus, a loop is created and the values get frozen. One of ...
... one of output classes change to 1, all output classes do not change any more. This function is implemented by means of OR and multiplexer gates. In other words, when one input of OR gate is being 1, second input of multiplexers are selected. Thus, a loop is created and the values get frozen. One of ...
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 ...
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.