about
Canalization and control in automata networks: body segmentation in Drosophila melanogasterInteractive machine learning for health informatics: when do we need the human-in-the-loop?Penalized likelihood phenotyping: unifying voxelwise analyses and multi-voxel pattern analyses in neuroimaging: penalized likelihood phenotypingMeta-interpretive learning of higher-order dyadic datalog: predicate invention revisitedLearning to recognize three-dimensional objects.Boosting regression estimators.A connectionist computational model for epistemic and temporal reasoning.Human-level concept learning through probabilistic program induction.Thousands of samples are needed to generate a robust gene list for predicting outcome in cancerComputational and evolutionary aspects of language.Probabilistic Analysis of Pattern Formation in Monotonic Self-Assembly.Will big data yield new mathematics? An evolving synergy with neuroscience.ROC-Boosting: A Feature Selection Method for Health Identification Using Tongue ImageSurvey on granularity clusteringLocalization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network.Self-Directed Learning: A Cognitive and Computational Perspective.Computational models of syntactic acquisition.Chemoinformatics as a Theoretical Chemistry Discipline.The simplicity principle in perception and cognition.Learnability theory.Optimal Behavior is Easier to Learn than the Truth.Detecting controlling nodes of boolean regulatory networks.A machine learning approach to create blocking criteria for record linkage.An empirical generative framework for computational modeling of language acquisition.Collegial Activity Learning between Heterogeneous Sensors.Protein analysis meets visual word recognition: a case for string kernels in the brain.The crystallizing substochastic sequential machine extractor: CrySSMEx.Complexity in language acquisition.Reduction from cost-sensitive ordinal ranking to weighted binary classification.Defining and simulating open-ended novelty: requirements, guidelines, and challenges.Why formal learning theory matters for cognitive science.Abductive learning of quantized stochastic processes with probabilistic finite automata.Quantum machine learning: a classical perspective.Determination and the no-free-lunch paradox.Performance and efficiency of memetic Pittsburgh learning classifier systems.A graphical model for evolutionary optimization.Response times seen as decompression times in Boolean concept use.Experiments with AdaBoost.RT, an improved boosting scheme for regression.On the nonlearnability of a single spiking neuron.Qualified predictions for microarray and proteomics pattern diagnostics with confidence machines.
P2860
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P2860
description
1984 nî lūn-bûn
@nan
1984 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
1984 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
1984年の論文
@ja
1984年論文
@yue
1984年論文
@zh-hant
1984年論文
@zh-hk
1984年論文
@zh-mo
1984年論文
@zh-tw
1984年论文
@wuu
name
A theory of the learnable
@ast
A theory of the learnable
@en
A theory of the learnable
@en-gb
type
label
A theory of the learnable
@ast
A theory of the learnable
@en
A theory of the learnable
@en-gb
prefLabel
A theory of the learnable
@ast
A theory of the learnable
@en
A theory of the learnable
@en-gb
P3181
P356
P1476
A theory of the learnable
@en
P304
P3181
P356
10.1145/1968.1972
P407
P577
1984-11-05T00:00:00Z