Lasso environment model combination for robust speech recognition by Xiao, Xiong, Li, Jinyu, Chng, Eng Siong, Li, Haizhou

“…Our study shows that Lasso usually shrinks to zero the weights of those mean supervectors not relevant to the test environment. …”
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Pro defunctis: Lassos frühe Toten- und Memorialkompositionen zwischen Individualisierung und Generalisierung by Leitmeir, C

“…This has a decisive impact on the tradition of the genre of the polyphonic Requiem, to which Orlando di Lasso made his first contribution only in his mid-forties. …”

High-dimensional data analysis with constraints by Zhou, Hanxiao

“…Thus, we studied scaled lasso and square root lasso on how they deal with the penalty level to the noise σ. …”
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Predicting wealth score from remote sensing satellite images and household survey data using deep learning by Shekhar, Shashank, Singh, Pratibha, Mishra, Rashmi, Kumar, Sunil

“…Here, the Lasso RM outperforms the others and is best suited for predicting the wealth score. …”
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Biomechanical study on arthroscopic biceps tenodesis fixation techniques by Lim, Gideon Boon Hao

“…Comparing the results, the lasso-loop suture technique had shown lower cyclic displacement, higher ultimate failure load and stiffness than the interference screw technique.…”
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Development of trading strategies using fundamental and technical analysis in SGX by Pan, Zhijin

“…This study first shows that use of dimensionality reduction method LASSO enhances the accuracy of the LSTM model and gives good predictions on stock prices. …”
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Soil liquefaction assessment using soft computing approaches based on capacity energy concept by Chen, Zhixiong, Li, Hongrui, Goh, Anthony Teck Chee, Wu, Chongzhi, Zhang, Wengang

“…In this study, based on the capacity energy database by Baziar et al. (2011), analyses have been carried out on a total of 405 previously published tests using soft computing approaches, including Ridge, Lasso & LassoCV, Random Forest, eXtreme Gradient Boost (XGBoost), and Multivariate Adaptive Regression Splines (MARS) approaches, to assess the capacity energy required to trigger liquefaction in sand and silty sands. …”
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Machine Learning Prediction of Treatment Response to Inhaled Corticosteroids in Asthma by Ong, Mei-Sing, Sordillo, Joanne E., Dahlin, Amber, McGeachie, Michael, Tantisira, Kelan, Wang, Alberta L., Lasky-Su, Jessica, Brilliant, Murray, Kitchner, Terrie, Roden, Dan M., Weiss, Scott T., Wu, Ann Chen

“…Using the SNPs identified, two machine learning models were developed to predict ICS response: (1) least absolute shrinkage and selection operator (LASSO) regression and (2) random forest. Results: The LASSO regression model achieved an AUC of 0.71 (95% CI 0.67–0.76; sensitivity: 0.57; specificity: 0.75) in an independent test cohort, and the random forest model achieved an AUC of 0.74 (95% CI 0.70–0.78; sensitivity: 0.70; specificity: 0.68). …”
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Risk prediction models to guide antibiotic prescribing : a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department by Wong, Joshua Guoxian, Aung, Aung-Hein, Lian, Weixiang, Lye, David Chien, Ooi, Chee-Kheong, Chow, Angela

“…The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67[95% CI: 0.59–0.74]). …”
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Topological data analysis for fake news detection by Deng, Ran

“…For this task, three individual models have been used: least absolute shrinkage and selection operator (LASSO) using 0th Dimensional Persistent Image (PI) vectors, Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Encoder Representations from Transformers (BERT). …”
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Variable selection for high-dimensional generalized varying-coefficient models by Lian, Heng

“…In particular, we show the adaptive group lasso estimator can correctly select important variables with probability approaching one and the convergence rates for the nonzero coefficients are the same as the oracle estimator (the estimator when the important variables are known before carrying out statistical analysis). …”
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A diagnostic host-specific transcriptome response for Mycoplasma pneumoniae pneumonia to guide pediatric patient treatment by Viz-Lasheras, S, Gómez-Carballa, A, Bello, X, Rivero-Calle, I, Dacosta, AI, Kaforou, M, Habgood-Coote, D, Cunnington, AJ, Emonts, M, Herberg, JA, Wright, VJ, Carrol, ED, Paulus, SC, Zenz, W, Kohlfürst, DS, Schweintzger, N, Van der Flier, M, de Groot, R, Schlapbach, LJ, Agyeman, P, Pollard, AJ, Fink, C, Kuijpers, TT, Anderson, S

“…Current diagnostic tests are time-consuming and have low specificity, leading clinicians to administer empirical antibiotics. Using a LASSO regression simulation approach and blood microarray data from 107 children with pneumonia (including 30 M. pneumoniae) we identify eight different transcriptomic signatures, ranging from 3-10 transcripts, that differentiate mycoplasma pneumonia from other bacterial/viral pneumonias with high accuracy (AUC: 0.84–0.95). …”

US federal resource allocations are inconsistent with concentrations of energy poverty by Batlle, Carlos, Heller, Peter, Knittel, Christopher, Schittekatte, Tim

“…Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. …”
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Variable selection in parametric and semiparametric models by Lin, Bingqing

“…Since the proposal of the least absolute shrinkage and selection operator (LASSO) (Tibshirani, 1996) and the smoothly clipped absolute deviation (SCAD) method (Fan and Li, 2001), there have been extensive developments on model selection based on penalized log-likelihood and the computational issues of solving these problems in linear models. …”
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Cognitive Systems and Signal Processing : 5th International Conference, ICCSIP 2020, Zhuhai, China, December 25-27, 2020, Revised Selected Papers / by ICCSIP (Conference) (5th : 2020 : Zhuhai, China), organiser 642897, Sun, Fuchun, 1964-, editor 600994, Liu, Huaping, 1976-, editor 600996, Fang, Bin, editor 642896

Assignment optimization of pandemic influenza antiviral drugs in Urban pharmacies by Zhang, Chijun, Du, Zhanwei, Cai, Qing, Yu, Limin, Li, Zhaohui, Bai, Yuan

“…The constrained optimization model is studied and solved using methods of willingness-to-travel model, L12 norm and network lasso, corresponding to each considerations. Taking Shanghai in a cohort of 11 million individuals as an example, we have shown the flexibility of the proposed multi-objective model, comparing with the traditional methods. …”
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Mathematical foundation of data science by Fang, Xiaowei

“…Of particular interest is the Lasso algorithm for sparse regressions. Last but not least, equipped withthe geometry of low-dimensional random projections, we wrapped up the book with a glimpse ofGaussian images of sets, projections of ellipsoids and random projections in the Grassmannian. …”
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Theory-guided machine learning to predict configurational energies of high distortion alloy systems by Huang, Xufa

“…State-of-the-art attempts at using CE with machine learning (ML) models like Lasso and Bayesian for selecting meaningful clusters show high prediction errors for these high distortion alloy systems, where the contributions of long-range effective cluster interactions (ECIs) to configurational energetics remain significant. …”
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Review and empirical analysis of machine learning-based software effort estimation by Rahman, Mizanur, Sarwar, Hasan, Abdul Kader, Md, Goncalves, Teresa C.F., Ting, Tin Tin

“…Additionally, comparative experiments were conducted using five commonly employed Machine Learning (ML) methods: K-Nearest Neighbor, Support Vector Machine, Random Forest, Logistic Regression, and LASSO Regression. The performance of these techniques was evaluated using five widely adopted accuracy metrics: Mean Squared Error (MSE), Mean Magnitude of Relative Error (MMRE), R-squared, Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). …”
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Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach by Mirjalili, Seyed Reza, Soltani, Sepideh, Meybodi, Zahra Heidari, Marques-Vidal, Pedro, Firouzabadi, Danial Dehghani, Eshraghi, Reza, Restrepo, David, Ghoshouni, Hamed, Sarebanhassanabadi, Mohammadtaghi

“…We also used three distinct embedded feature selection methods: LASSO, Random Forest feature selection, and the Boruta algorithm, to evaluate and compare surrogate markers of insulin resistance in predicting CAD. …”
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