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Overfitting

Known as: Underfitting, Over-fitted, Overfit 
In statistics and machine learning, one of the most common tasks is to fit a "model" to a set of training data, so as to be able to make reliable… 
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Related topics

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AdaBoostB-splineBackpropagationBacktesting

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Intelligent Training Algorithm for Artificial Neural Network EEG Classifications
Artificial neural networks (ANN) have been widely used in classification. They are complicated networks due to the training… 
2016
2016
VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task
We present the Vancouver Event and Relation System for Extraction (VERSE) 1 as a competing system for three subtasks of the… 
2014
2014
Approaches in Highly Parameterized Inversion: bgaPEST, a Bayesian Geostatistical Approach Implementation With PEST?Documentation and Instructions
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Review
2014
Review
2014
Survey of Classification Techniques in Data Mining
Classification in data mining is a technique based on machine learning algorithms which uses mathematics, statistics, probability… 
2013
2013
Understanding the dropout strategy and analyzing its effectiveness on LVCSR
The work by Hinton et al shows that the dropout strategy can greatly improve the performance of neural networks as well as… 
2011
2011
Learning Hierarchical Translation Structure with Linguistic Annotations
While it is generally accepted that many translation phenomena are correlated with linguistic structures, employing linguistic… 
2010
2010
Interaction and Personalization of Criteria in Recommender Systems
A user's informational need and preferences can be modeled by criteria, which in turn can be used to prioritize candidate results… 
2006
2006
Multi-Criterion Active Learning in Conditional Random Fields
Conditional random fields (CRFs), which are popular supervised learning models for many natural language processing (NLP) tasks… 
2005
2005
Sequential Genetic Search for Ensemble Feature Selection
Ensemble learning constitutes one of the main directions in machine learning and data mining. Ensembles allow us to achieve… 
2003
2003
Simultaneous 1 D inversion of loop – loop electromagnetic data for magnetic susceptibility and electrical conductivity
Magnetic susceptibility affects electromagnetic (EM) loop–loop observations in ways that cannot be replicated by conductive…