Abstract:
:We present a new supervised learning procedure for ensemble machines, in which outputs of predictors, trained on different distributions, are combined by a dynamic classifier combination model. This procedure may be viewed as either a version of mixture of experts (Jacobs, Jordan, Nowlan, & Hintnon, 1991), applied to classification, or a variant of the boosting algorithm (Schapire, 1990). As a variant of the mixture of experts, it can be made appropriate for general classification and regression problems by initializing the partition of the data set to different experts in a boostlike manner. If viewed as a variant of the boosting algorithm, its main gain is the use of a dynamic combination model for the outputs of the networks. Results are demonstrated on a synthetic example and a digit recognition task from the NIST database and compared with classifical ensemble approaches.
journal_name
Neural Computjournal_title
Neural computationauthors
Avnimelech R,Intrator Ndoi
10.1162/089976699300016737subject
Has Abstractpub_date
1999-02-15 00:00:00pages
483-97issue
2eissn
0899-7667issn
1530-888Xjournal_volume
11pub_type
杂志文章abstract::Neuroscience is progressing vigorously, and knowledge at different levels of description is rapidly accumulating. To establish relationships between results found at these different levels is one of the central challenges. In this simulation study, we demonstrate how microscopic cellular properties, taking the example...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976699300016377
更新日期:1999-07-01 00:00:00
abstract::This article studies a general theory of estimating functions of independent component analysis when the independent source signals are temporarily correlated. Estimating functions are used for deriving both batch and on-line learning algorithms, and they are applicable to blind cases where spatial and temporal probab...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015079
更新日期:2000-09-01 00:00:00
abstract::We study the learning of an external signal by a neural network and the time to forget it when this network is submitted to noise. The presentation of an external stimulus to the recurrent network of binary neurons may change the state of the synapses. Multiple presentations of a unique signal lead to its learning. Th...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01286
更新日期:2020-07-01 00:00:00
abstract::The need to reason about uncertainty in large, complex, and multimodal data sets has become increasingly common across modern scientific environments. The ability to transform samples from one distribution journal_title:Neural computation pub_type: 杂志文章 doi:10.1162/neco_a_01172 更新日期:2019-04-01 00:00:00
abstract::The problem of designing input signals for optimal generalization is called active learning. In this article, we give a two-stage sampling scheme for reducing both the bias and variance, and based on this scheme, we propose two active learning methods. One is the multipoint search method applicable to arbitrary models...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300014773
更新日期:2000-12-01 00:00:00
abstract::Pharmacologically isolated GABAergic irregular spiking and stuttering interneurons in the mouse visual cortex display highly irregular spike times, with high coefficients of variation approximately 0.9-3, in response to a depolarizing, constant current input. This is in marked contrast to cortical pyramidal cells, whi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.20.1.44
更新日期:2008-01-01 00:00:00
abstract::We describe a model of short-term synaptic depression that is derived from a circuit implementation. The dynamics of this circuit model is similar to the dynamics of some theoretical models of short-term depression except that the recovery dynamics of the variable describing the depression is nonlinear and it also dep...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603762552942
更新日期:2003-02-01 00:00:00
abstract::We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In thi...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00533
更新日期:2014-01-01 00:00:00
abstract::Integrate-and-express models of synaptic plasticity propose that synapses integrate plasticity induction signals before expressing synaptic plasticity. By discerning trends in their induction signals, synapses can control destabilizing fluctuations in synaptic strength. In a feedforward perceptron framework with binar...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00889
更新日期:2016-11-01 00:00:00
abstract::We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero corre...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976603321192068
更新日期:2003-03-01 00:00:00
abstract::We derive a synaptic weight update rule for learning temporally precise spike train-to-spike train transformations in multilayer feedforward networks of spiking neurons. The framework, aimed at seamlessly generalizing error backpropagation to the deterministic spiking neuron setting, is based strictly on spike timing ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00829
更新日期:2016-05-01 00:00:00
abstract::Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)-based approaches and autoencoder (AE)-based approaches. CCA-based approaches...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00801
更新日期:2016-02-01 00:00:00
abstract::Field models provide an elegant mathematical framework to analyze large-scale patterns of neural activity. On the microscopic level, these models are usually based on either a firing-rate picture or integrate-and-fire dynamics. This article shows that in spite of the large conceptual differences between the two types ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660260028656
更新日期:2002-07-01 00:00:00
abstract::To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an i...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco_a_01239
更新日期:2019-12-01 00:00:00
abstract::We analyze convergence of the expectation maximization (EM) and variational Bayes EM (VBEM) schemes for parameter estimation in noisy linear models. The analysis shows that both schemes are inefficient in the low-noise limit. The linear model with additive noise includes as special cases independent component analysis...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766054322991
更新日期:2005-09-01 00:00:00
abstract::As neural activity is transmitted through the nervous system, neuronal noise degrades the encoded information and limits performance. It is therefore important to know how information loss can be prevented. We study this question in the context of neural population codes. Using Fisher information, we show how informat...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00227
更新日期:2012-02-01 00:00:00
abstract::Natural gradient learning is known to be efficient in escaping plateau, which is a main cause of the slow learning speed of neural networks. The adaptive natural gradient learning method for practical implementation also has been developed, and its advantage in real-world problems has been confirmed. In this letter, w...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976604322742065
更新日期:2004-02-01 00:00:00
abstract::Multiple adjacent, roughly mirror-image topographic maps are commonly observed in the sensory neocortex of many species. The cortical regions occupied by these maps are generally believed to be determined initially by genetically controlled chemical markers during development, with thalamocortical afferent activity su...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766053491904
更新日期:2005-05-01 00:00:00
abstract::We propose a novel paradigm for spike train decoding, which avoids entirely spike sorting based on waveform measurements. This paradigm directly uses the spike train collected at recording electrodes from thresholding the bandpassed voltage signal. Our approach is a paradigm, not an algorithm, since it can be used wit...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2008.02-07-478
更新日期:2008-04-01 00:00:00
abstract::In "Isotropic Sequence Order Learning" (pp. 831-864 in this issue), we introduced a novel algorithm for temporal sequence learning (ISO learning). Here, we embed this algorithm into a formal nonevaluating (teacher free) environment, which establishes a sensor-motor feedback. The system is initially guided by a fixed r...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660360581930
更新日期:2003-04-01 00:00:00
abstract::The ability to achieve high swimming speed and efficiency is very important to both the real lamprey and its robotic implementation. In previous studies, we used evolutionary algorithms to evolve biologically plausible connectionist swimming controllers for a simulated lamprey. This letter investigates the robustness ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/neco.2007.19.6.1568
更新日期:2007-06-01 00:00:00
abstract::Tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during egomotion. In this study, we examine whether a simplified linear model based on the organization principles in tangential neurons can be used to estimate egomotion from the optic flow. We present a theory for the cons...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/0899766041941899
更新日期:2004-11-01 00:00:00
abstract::In this letter, we perform a complete and in-depth analysis of Lorentzian noises, such as those arising from [Formula: see text] and [Formula: see text] channel kinetics, in order to identify the source of [Formula: see text]-type noise in neurological membranes. We prove that the autocovariance of Lorentzian noise de...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_01067
更新日期:2018-07-01 00:00:00
abstract::We study the expressive power of positive neural networks. The model uses positive connection weights and multiple input neurons. Different behaviors can be expressed by varying the connection weights. We show that in discrete time and in the absence of noise, the class of positive neural networks captures the so-call...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00789
更新日期:2015-12-01 00:00:00
abstract::Recently there has been great interest in sparse representations of signals under the assumption that signals (data sets) can be well approximated by a linear combination of few elements of a known basis (dictionary). Many algorithms have been developed to find such representations for one-dimensional signals (vectors...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00385
更新日期:2013-01-01 00:00:00
abstract::A spiking neuron "computes" by transforming a complex dynamical input into a train of action potentials, or spikes. The computation performed by the neuron can be formulated as dimensional reduction, or feature detection, followed by a nonlinear decision function over the low-dimensional space. Generalizations of the ...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/08997660360675017
更新日期:2003-08-01 00:00:00
abstract::The emergence of synchrony in the activity of large, heterogeneous networks of spiking neurons is investigated. We define the robustness of synchrony by the critical disorder at which the asynchronous state becomes linearly unstable. We show that at low firing rates, synchrony is more robust in excitatory networks tha...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/089976600300015286
更新日期:2000-07-01 00:00:00
abstract::A variant of spiking neural P systems with positive or negative weights on synapses is introduced, where the rules of a neuron fire when the potential of that neuron equals a given value. The involved values-weights, firing thresholds, potential consumed by each rule-can be real (computable) numbers, rational numbers,...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00022
更新日期:2010-10-01 00:00:00
abstract::Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks o...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00151
更新日期:2011-08-01 00:00:00
abstract::The goal of sufficient dimension reduction in supervised learning is to find the low-dimensional subspace of input features that contains all of the information about the output values that the input features possess. In this letter, we propose a novel sufficient dimension-reduction method using a squared-loss variant...
journal_title:Neural computation
pub_type: 杂志文章
doi:10.1162/NECO_a_00407
更新日期:2013-03-01 00:00:00