Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
47
Sequential Model-Based Ensemble Optimization
"... One of the most tedious tasks in the applica-tion of machine learning is model selection, i.e. hyperparameter selection. Fortunately, recent progress has been made in the automation of this process, through the use of sequential model-based optimization (SMBO) methods. This can be used to optimize a ..."
Abstract
- Add to MetaCart
a cross-validation perfor-mance of a learning algorithm over the value of its hyperparameters. However, it is well known that ensembles of learned models almost consis-tently outperform a single model, even if prop-erly selected. In this paper, we thus propose an extension of SMBO methods
A new look at psychological climate and its relationship to job involvement, effort, and performance
- Journal of Applied Psychology
, 1996
"... This study investigated the process by which employee perceptions of the organizational environment are related to job involvement, effort, and performance. The researchers developed an operational definition of psychological climate that was based on how em-ployees perceive aspects of the organizat ..."
Abstract
-
Cited by 94 (1 self)
- Add to MetaCart
involvement, which in turn was related to effort. Effort was also related to work performance. Results revealed that a modest but statistically significant effect of job involvement on perfor-mance became nonsignificant when effort was inserted into the model, indicating the mediating effect of effort
Cross-cultural validity of the Inventory of School Motivation (ISM) in Chinese and Filipino samples
- Child Indicators Research
, 2012
"... # The Author(s) 2011. This article is published with open access at Springerlink.com Abstract Students ’ achievement goals in school have received increasing research attention because they have been shown to be important in predicting important outcomes. As such, there has been a growing interest i ..."
Abstract
-
Cited by 2 (1 self)
- Add to MetaCart
Motivation (ISM, McInerney et al. American Educational Research Journal 34:207-236, 1997) in the Hong Kong Chinese and Philippine contexts using both within-network and between-network approaches to construct validation. The ISM measures four types of achievement goals: mastery, perfor-mance, social
Assessing stability of gene selection in microarray data analysis
- BMC BIOINFORMATICS
, 2006
"... Background. The number of genes declared differentially expressed is a ran-dom variable and its variability can be assessed by resampling techniques. Another important stability indicator is the frequency with which a given gene is selected across subsamples. We have conducted studies to assess sta- ..."
Abstract
-
Cited by 24 (4 self)
- Add to MetaCart
represents a tool for reducing the set of ini-tially selected genes to those with a sufficiently high selection frequency. Us-ing cross-validation it is also possible to assess variability of different perfor-mance indicators. Stability properties of several multiple testing procedures are described
Rights Creative Commons: Attribution 3.0 Hong Kong License Cross-Cultural Validation of the Inventory of School Motivation (ISM) in the Asian Setting: Hong Kong and the Philippines
, 2011
"... # The Author(s) 2011. This article is published with open access at Springerlink.com Abstract Students ’ achievement goals in school have received increasing research attention because they have been shown to be important in predicting important outcomes. As such, there has been a growing interest i ..."
Abstract
- Add to MetaCart
Motivation (ISM, McInerney et al. American Educational Research Journal 34:207-236, 1997) in the Hong Kong Chinese and Philippine contexts using both within-network and between-network approaches to construct validation. The ISM measures four types of achievement goals: mastery, perfor-mance, social
Sequence-based prediction of protein domains. Nucleic Acids Research 32(12):3522–3530
, 2004
"... Guessing the boundaries of structural domains has been an important and challenging problem in experimental and computational structural biology. Predictions were based on intuition, biochemical properties, statistics, sequence homology and other aspects of predicted protein structure. Here, we intr ..."
Abstract
-
Cited by 31 (1 self)
- Add to MetaCart
intro-duced CHOPnet, a de novo method that predicts structural domains in the absence of homology to known domains. Our method was based on neural networks and relied exclusively on information available for all proteins. Evaluating sustained perfor-mance through rigorous cross-validation on proteins
On over-fitting in model selection and subsequent selection bias in performance evaluation
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2010
"... Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can be broken down into bias ..."
Abstract
-
Cited by 20 (2 self)
- Add to MetaCart
Model selection strategies for machine learning algorithms typically involve the numerical optimisation of an appropriate model selection criterion, often based on an estimator of generalisation performance, such as k-fold cross-validation. The error of such an estimator can be broken down
Predicting Student Performance in Solving Parameterized Exercises
"... Abstract. In this paper, we compare pioneer methods of educational data mining field with recommender systems techniques for predict-ing student performance. Additionally, we study the importance of in-cluding students ’ attempt time sequences of parameterized exercises. The approaches we use are Ba ..."
Abstract
- Add to MetaCart
Components (KCs) and test the methods using cross-validation. In this work, we focus on predicting students ’ performance in parameterized exercises. Our experi-ments shows that advanced recommender system techniques are as accu-rate as the pioneer methods in predicting student performance. Also, our studies
SWATCS65: Sentiment Classification Using an Ensemble of Class Projects
"... This paper presents the SWATCS65 ensem-ble classifier used to identify the sentiment of tweets. The classifier was trained and tested using data provided by Semeval-2015, Task 10, subtask B with the goal to label the sen-timent of an entire tweet. The ensemble was constructed from 26 classifiers, ea ..."
Abstract
- Add to MetaCart
variability in the final perfor-mance of each of these classifiers, which were combined using a weighted voting scheme with weights correlated with performance us-ing 5-fold cross-validation on the provided training data. The system performed very well, achieving an F1 score of 61.89. 1
Variational bayes logistic regression as regularized fusion for NIST sre 2010
- in Proc. Odyssey: the Speaker and Language Recognition Workshop
, 2012
"... Fusion of the base classifiers is seen as a way to achieve high performance in state-of-the-art speaker verification systems. Typically, we are looking for base classifiers that would be com-plementary. We might also be interested in reinforcing good base classifiers by including others that are sim ..."
Abstract
-
Cited by 10 (8 self)
- Add to MetaCart
that are similar to them. In any case, the final ensemble size is typically small and has to be formed based on some rules of thumb. We are interested to find out a subset of classifiers that has a good generalization perfor-mance. We approach the problem from sparse learning point of view. We assume that the true
Results 1 - 10
of
47
