Artificial Intelligence (Latest Articles)

Latest indexed articles for 'Artificial Intelligence'

Articles 101 to 110 of 200:

Error minimized extreme learning machine with growth of hidden nodes and incremental learning.

8 Jul 2009 One of the open problems in neural network research is how to automatically determine network architectures for given applications. In this brief, we propose a simple and efficient approach to automatically determine the number of hidden nodes in ...
rec_pub_19596632-error-minimized-extreme-learning-machine-growth-hidden-nodes.htm

Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

8 Jul 2009 The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network ...
rec_pub_19596631-simple-artificial-neural-networks-match-probability-exploit-explore.htm

Genome-scale identification of Legionella pneumophila effectors using a machine learning approach.

8 Jul 2009 A large number of highly pathogenic bacteria utilize secretion systems to translocate effector proteins into host cells. Using these effectors, the bacteria subvert host cell processes during infection. Legionella pneumophila translocates effectors ...
rec_pub_19593377-genome-scale-identification-legionella-pneumophila-effectors-using.htm

Universal perceptron and DNA-like learning algorithm for binary neural networks: non-LSBF implementation.

5 Jul 2009 Implementing linearly nonseparable Boolean functions (non-LSBF) has been an important and yet challenging task due to the extremely high complexity of this kind of functions and the exponentially increasing percentage of the number of non-LSBF in ...
rec_pub_19589746-universal-perceptron-dna-like-learning-algorithm-binary-neural.htm

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data.

5 Jul 2009 BACKGROUND: One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its ...
rec_pub_19594885-an-ensemble-learning-approach-reverse-engineering-transcriptional.htm

Comparison of feature selection and classification for MALDI-MS data.

5 Jul 2009 INTRODUCTION: In the classification of Mass Spectrometry (MS) proteomics data, peak detection, feature selection, and learning classifiers are critical to classification accuracy. To better understand which methods are more accurate when classifying ...
rec_pub_19594880-comparison-feature-selection-classification-maldi-ms-data.htm

Prediction of DNA-binding residues from protein sequence information using random forests.

5 Jul 2009 BACKGROUND: Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding ...
rec_pub_19594868-prediction-dna-binding-residues-protein-sequence-information-using.htm

RECIST and WHO criteria evaluation of cervical, thoracic and abdominal lymph nodes in patients with malignant lymphoma: manual versus semi-automated measurement on standard MDCT slices.

4 Jul 2009 PURPOSE: Since semi-automated lesion quantification may be more precise than manual uni- and bidimensional measurements, the purpose of this study was to compare semi-automated with manual evaluations of cervical, thoracic and abdominal lymph nodes ...
rec_pub_19582654-recist-criteria-evaluation-cervical-thoracic-abdominal-lymph-nodes.htm

A new learning paradigm: learning using privileged information.

Jul 2009 In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an ...
rec_pub_19632812-a-new-learning-paradigm-learning-using-privileged-information.htm

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

Jul 2009 Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own ...
rec_pub_19604672-neural-networks-multiple-general-neuron-models-hybrid-computational.htm

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