Log in

Relevant bibliographies by topics / Spectral Learning

Contents

Journal articles
Dissertations / Theses
Books
Book chapters
Conference papers
Reports

Academic literature on the topic 'Spectral Learning'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 April 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Spectral Learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Spectral Learning"

1

Sharma, Kaushal, Harinder P. Singh, Ranjan Gupta, et al. "Stellar spectral interpolation using machine learning." Monthly Notices of the Royal Astronomical Society 496, no. 4 (2020): 5002–16. http://dx.doi.org/10.1093/mnras/staa1809.

Full text

Abstract:

ABSTRACT Theoretical stellar spectra rely on model stellar atmospheres computed based on our understanding of the physical laws at play in the stellar interiors. These models, coupled with atomic and molecular line databases, are used to generate theoretical stellar spectral libraries (SSLs) comprising of stellar spectra over a regular grid of atmospheric parameters (temperature, surface gravity, abundances) at any desired resolution. Another class of SSLs is referred to as empirical spectral libraries; these contain observed spectra at limited resolution. SSLs play an essential role in derivi

APA, Harvard, Vancouver, ISO, and other styles

2

Xu, Laixiang, Jun Xie, Fuhong Cai, and Jingjin Wu. "Spectral Classification Based on Deep Learning Algorithms." Electronics 10, no. 16 (2021): 1892. http://dx.doi.org/10.3390/electronics10161892.

Full text

Abstract:

Convolutional neural networks (CNN) can achieve accurate image classification, indicating the current best performance of deep learning algorithms. However, the complexity of spectral data limits the performance of many CNN models. Due to the potential redundancy and noise of the spectral data, the standard CNN model is usually unable to perform correct spectral classification. Furthermore, deeper CNN architectures also face some difficulties when other network layers are added, which hinders the network convergence and produces low classification accuracy. To alleviate these problems, we prop

APA, Harvard, Vancouver, ISO, and other styles

3

Li, Jian, Yong Liu, and Weiping Wang. "Automated Spectral Kernel Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 4618–25. http://dx.doi.org/10.1609/aaai.v34i04.5892.

Full text

Abstract:

The generalization performance of kernel methods is largely determined by the kernel, but spectral representations of stationary kernels are both input-independent and output-independent, which limits their applications on complicated tasks. In this paper, we propose an efficient learning framework that incorporates the process of finding suitable kernels and model training. Using non-stationary spectral kernels and backpropagation w.r.t. the objective, we obtain favorable spectral representations that depends on both inputs and outputs. Further, based on Rademacher complexity, we derive data-

APA, Harvard, Vancouver, ISO, and other styles

4

Mehrkanoon, Siamak, Xiaolin Huang, and Johan A. K. Suykens. "Indefinite kernel spectral learning." Pattern Recognition 78 (June 2018): 144–53. http://dx.doi.org/10.1016/j.patcog.2018.01.014.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Yuan, Debao, Ling Wu, Huinan Jiang, Bingrui Zhang, and Jian Li. "LSTNet: A Reference-Based Learning Spectral Transformer Network for Spectral Super-Resolution." Sensors 22, no. 5 (2022): 1978. http://dx.doi.org/10.3390/s22051978.

Full text

Abstract:

Hyperspectral images (HSIs) are data cubes containing rich spectral information, making them beneficial to many Earth observation missions. However, due to the limitations of the associated imaging systems and their sensors, such as the swath width and revisit period, hyperspectral imagery over a large coverage area cannot be acquired in a short amount of time. Spectral super-resolution (SSR) is a method that involves learning the relationship between a multispectral image (MSI) and an HSI, based on the overlap region, followed by reconstruction of the HSI by making full use of the large swath

APA, Harvard, Vancouver, ISO, and other styles

6

Liang, Mingyang, Xiaoyang Guo, Hongsheng Li, Xiaogang Wang, and You Song. "Unsupervised Cross-Spectral Stereo Matching by Learning to Synthesize." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8706–13. http://dx.doi.org/10.1609/aaai.v33i01.33018706.

Full text

Abstract:

Unsupervised cross-spectral stereo matching aims at recovering disparity given cross-spectral image pairs without any depth or disparity supervision. The estimated depth provides additional information complementary to original images, which can be helpful for other vision tasks such as tracking, recognition and detection. However, there are large appearance variations between images from different spectral bands, which is a challenge for cross-spectral stereo matching. Existing deep unsupervised stereo matching methods are sensitive to the appearance variations and do not perform well on cros

APA, Harvard, Vancouver, ISO, and other styles

7

Foschino, S., O. Berné, and C. Joblin. "Learning mid-IR emission spectra of polycyclic aromatic hydrocarbon populations from observations." Astronomy & Astrophysics 632 (December 2019): A84. http://dx.doi.org/10.1051/0004-6361/201935085.

Full text

Abstract:

Context. The James Webb Space Telescope (JWST) will deliver an unprecedented quantity of high-quality spectral data over the 0.6−28 μm range. It will combine sensitivity, spectral resolution, and spatial resolution. Specific tools are required to provide efficient scientific analysis of such large data sets. Aims. Our aim is to illustrate the potential of unsupervised learning methods to get insights into chemical variations in the populations that carry the aromatic infrared bands (AIBs), more specifically polycyclic aromatic hydrocarbon (PAH) species and carbonaceous very small grains (VSGs)

APA, Harvard, Vancouver, ISO, and other styles

8

Huber, Florian, Lars Ridder, Stefan Verhoeven, et al. "Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships." PLOS Computational Biology 17, no. 2 (2021): e1008724. http://dx.doi.org/10.1371/journal.pcbi.1008724.

Full text

Abstract:

Spectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm—Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract

APA, Harvard, Vancouver, ISO, and other styles

9

Chen, Tieqiao, Xiuqin Su, Haiwei Li, et al. "Learning a Fully Connected U-Net for Spectrum Reconstruction of Fourier Transform Imaging Spectrometers." Remote Sensing 14, no. 4 (2022): 900. http://dx.doi.org/10.3390/rs14040900.

Full text

Abstract:

Fourier transform imaging spectrometers (FTISs) are widely used in global hyperspectral remote sensing due to the advantages of high stability, high throughput, and high spectral resolution. Spectrum reconstruction (SpecR) is a classic problem of FTISs determining the acquired data quality and application potential. However, the state-of-the-art SpecR algorithms were restricted by the length of maximum optical path difference (MOPD) of FTISs and apodization processing, resulting in a decrease in spectral resolution; thus, the applications of FTISs were limited. In this study, a deep learning S

APA, Harvard, Vancouver, ISO, and other styles

10

Wen, Guoqiu, Yonghua Zhu, and Wei Zheng. "Spectral representation learning for one-step spectral rotation clustering." Neurocomputing 406 (September 2020): 361–70. http://dx.doi.org/10.1016/j.neucom.2019.09.108.

Full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "Spectral Learning"

1

Shortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Smith, Natalie T. (Natalie Tamika) 1978. "Interactive spectral analysis learning module." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8600.

Full text

Abstract:

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.<br>Includes bibliographical references (leaf 103).<br>Due to increased demand for interactive learning opportunities for engineering students, an interactive spectral analysis learning module was developed for the course Biomedical Signal and Image Processing (HST582J/6.555J/16.456J). The design of this module is based on the Star Legacy model, a pedagogical framework that promotes the creation of guided learning environments that use applications as the context for focused learn

APA, Harvard, Vancouver, ISO, and other styles

3

Boots, Byron. "Spectral Approaches to Learning Predictive Representations." Research Showcase @ CMU, 2012. http://repository.cmu.edu/dissertations/131.

Full text

Abstract:

A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must obtain an accurate environment model, and then plan to maximize reward. However, for complex domains, specifying a model by hand can be a time consuming process. This motivates an alternative approach: learning a model directly from observations. Unfortunately, learning algorithms often recover a model that is too inaccurate to support planning or too large and complex for planning to succeed; or, they require excessive prior domain knowledge

APA, Harvard, Vancouver, ISO, and other styles

4

Drake, Adam C. "Practical Improvements in Applied Spectral Learning." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2546.

Full text

Abstract:

Spectral learning algorithms, which learn an unknown function by learning a spectral representation of the function, have been widely used in computational learning theory to prove many interesting learnability results. These algorithms have also been successfully used in real-world applications. However, previous work has left open many questions about how to best use these methods in real-world learning scenarios. This dissertation presents several significant advances in real-world spectral learning. It presents new algorithms for finding large spectral coefficients (a key sub-problem in sp

APA, Harvard, Vancouver, ISO, and other styles

5

Alexander, Miranda Abhilash. "Spectral factor model for time series learning." Doctoral thesis, Universite Libre de Bruxelles, 2011. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209812.

Full text

Abstract:

Today's computerized processes generate<p>massive amounts of streaming data.<p>In many applications, data is collected for modeling the processes. The process model is hoped to drive objectives such as decision support, data visualization, business intelligence, automation and control, pattern recognition and classification, etc. However, we face significant challenges in data-driven modeling of processes. Apart from the errors, outliers and noise in the data measurements, the main challenge is due to a large dimensionality, which is the number of variables each data sample measures. The sampl

APA, Harvard, Vancouver, ISO, and other styles

6

Manjunatha, Bharadwaj Sandhya. "Land Cover Quantification using Autoencoder based Unsupervised Deep Learning." Thesis, Virginia Tech, 2020. http://hdl.handle.net/10919/99861.

Full text

Abstract:

This work aims to develop a deep learning model for land cover quantification through hyperspectral unmixing using an unsupervised autoencoder. Land cover identification and classification is instrumental in urban planning, environmental monitoring and land management. With the technological advancements in remote sensing, hyperspectral imagery which captures high resolution images of the earth's surface across hundreds of wavelength bands, is becoming increasingly popular. The high spectral information in these images can be analyzed to identify the various target materials present in the ima

APA, Harvard, Vancouver, ISO, and other styles

7

Araya, Valdivia Ernesto. "Kernel spectral learning and inference in random geometric graphs." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM020.

Full text

Abstract:

Cette thèse comporte deux objectifs. Un premier objectif concerne l’étude des propriétés de concentration des matrices à noyau, qui sont fondamentales dans l’ensemble des méthodes à noyau. Le deuxième objectif repose quant à lui sur l’étude des problèmes d’inférence statistique dans le modèle des graphes aléatoires géométriques. Ces deux objectifs sont liés entre eux par le formalisme du graphon, qui permet représenter un graphe par un noyau. Nous rappelons les rudiments du modèle du graphon dans le premier chapitre. Le chapitre 2 présente des bornes précises pour les valeurs propres individue

APA, Harvard, Vancouver, ISO, and other styles

8

Wang, Hongfang. "Non-rigid motion behaviour learning : a spectral and graphical approach." Thesis, University of York, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.441066.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Moro, Viggo. "Deep-learning image reconstruction for photon-counting spectral computed tomography." Thesis, KTH, Skolan för teknikvetenskap (SCI), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297560.

Full text

Abstract:

X-ray computed tomography (CT) has since its introduction in the early 1970s become one of the most important tools used for medical imaging. In CT, a large number of x-ray attenuation measurements are combined and reconstructed to form a three-dimensional image of the targeted area. In the recent years, a new type of detector called photon counting detector (PCD) has attracted considerable interest. This new type of detector acquires spectral information is associated with several benefits and has shown to be very valuable. Furthermore, the use of deep learning to reconstruct images produced

APA, Harvard, Vancouver, ISO, and other styles

10

Williams, Alyssa. "Hybrid Recommender Systems via Spectral Learning and a Random Forest." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3666.

Full text

Abstract:

We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interprete

APA, Harvard, Vancouver, ISO, and other styles

More sources

Books on the topic "Spectral Learning"

1

Pierangelo, Roger. Teaching students with autism spectrum disorders. Corwin Press, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

2

Pierangelo, Roger. Teaching students with autism spectrum disorders. Corwin Press, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

3

Pierangelo, Roger. Teaching students with autism spectrum disorders. Corwin Press, 2008.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

4

Alexander, Kay. Learning to look and create: The SPECTRA program. Dale Seymour Publications, 1989.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

5

Alexander, Kay. Learning to look and create: The SPECTRA program. Dale Seymour Publications, 1987.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

6

Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

7

Bolla, Marianna. Spectral Clustering and Biclustering: Learning Large Graphs and Contingency Tables. Wiley & Sons, Incorporated, John, 2013.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

8

Spectrum Learning Letters. Spectrum, 2006.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

9

Publishing, School Specialty. Spectrum Learning Letters (Spectrum Preschool Series). Spectrum, 2001.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

10

Spectrum Spelling 4 (Mcraw-Hill Learning Materials Spectrum). Spectrum, 2002.

Find full text

APA, Harvard, Vancouver, ISO, and other styles

More sources

Book chapters on the topic "Spectral Learning"

1

Cleophas, Ton J., and Aeilko H. Zwinderman. "Spectral Plots." In Machine Learning in Medicine. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-7869-6_15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Martin, Eric, Samuel Kaski, Fei Zheng, et al. "Spectral Clustering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_771.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ding, Guoru, Siyu Zhai, Xiaoming Chen, Yuming Zhang, and Chao Liu. "Robust Spectral-Temporal Two-Dimensional Spectrum Prediction." In Machine Learning and Intelligent Communications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52730-7_40.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Bouchachia, Abdelhamid, and Markus Prossegger. "Incremental Spectral Clustering." In Learning in Non-Stationary Environments. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4419-8020-5_4.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Mannor, Shie, Xin Jin, Jiawei Han, et al. "K-Way Spectral Clustering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_427.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Langone, Rocco, Raghvendra Mall, Carlos Alzate, and Johan A. K. Suykens. "Kernel Spectral Clustering and Applications." In Unsupervised Learning Algorithms. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24211-8_6.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Tripathy, B. K., S. Anveshrithaa, and Shrusti Ghela. "Spectral Clustering." In Unsupervised Learning Approaches for Dimensionality Reduction and Data Visualization. CRC Press, 2021. http://dx.doi.org/10.1201/9781003190554-10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Jiang, Wenhao, and Fu-lai Chung. "Transfer Spectral Clustering." In Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33486-3_50.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Kannan, Ravindran, Hadi Salmasian, and Santosh Vempala. "The Spectral Method for General Mixture Models." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_30.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Achlioptas, Dimitris, and Frank McSherry. "On Spectral Learning of Mixtures of Distributions." In Learning Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11503415_31.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Spectral Learning"

1

Gramstad, Odin, and Elzbieta Bitner-Gregersen. "Predicting Extreme Waves From Wave Spectral Properties Using Machine Learning." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-96061.

Full text

Abstract:

Abstract An important question in the context of rogue waves is whether the statistical properties of individual waves, and in particular the probability of extreme and rogue waves, can be linked to the properties of the underlying wave spectrum of the relevant sea state. It has been suggested that a narrow wave spectrum (in frequency or direction) combined with a large wave steepness may lead to increased occurrence of extreme waves. Parameters based on the ratio of the wave steepness to the spectral band-widths have therefore been suggested as indicators of increased probability of extreme w

APA, Harvard, Vancouver, ISO, and other styles

2

Xue, Hui, Zheng-Fan Wu, and Wei-Xiang Sun. "Deep Spectral Kernel Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/558.

Full text

Abstract:

Recently, spectral kernels have attracted wide attention in complex dynamic environments. These advanced kernels mainly focus on breaking through the crucial limitation on locality, that is, the stationarity and the monotonicity. But actually, owing to the inefficiency of shallow models in computational elements, they are more likely unable to accurately reveal dynamic and potential variations. In this paper, we propose a novel deep spectral kernel network (DSKN) to naturally integrate non-stationary and non-monotonic spectral kernels into elegant deep architectures in an interpretable way, wh

APA, Harvard, Vancouver, ISO, and other styles

3

Ling, Xiao, Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. "Spectral domain-transfer learning." In the 14th ACM SIGKDD international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401951.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Narayan, Shashi, and Shay B. Cohen. "Optimizing Spectral Learning for Parsing." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1146.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Li, Hongmin, Xiucai Ye, Akira Imakura, and Tetsuya Sakurai. "Ensemble Learning for Spectral Clustering." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00131.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Vuong, Luat, and Hobson Lane. "Nonlinear spectral preprocessing for small-brain machine learning." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2530789.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Zerafa, C. "DNN Application For Pseudo-Spectral FWI." In First EAGE/PESGB Workshop Machine Learning. EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201803015.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Monroy, Brayan, Jorge Bacca, and Henry Arguello. "Deep Low-Dimensional Spectral Image Representation for Compressive Spectral Reconstruction." In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2021. http://dx.doi.org/10.1109/mlsp52302.2021.9596541.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Xu, Xiao-Hua, Ping He, and Ling Chen. "Learning spectral graph mapping for classification." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580573.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Ping He, Xiao-Hua Xu, and Ling Chen. "Tree classifier in spectral space." In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212576.

Full text

APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Spectral Learning"

1

Jayaweera, Sudharman. Machine Learning-Aided, Robust Wideband Spectrum Sensing for Cognitive Radios. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada625246.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Hwa, Yue-Yi, Michelle Kaffenberger, and Jason Silberstein. Aligning Levels of Instruction with Goals and the Needs of Students (ALIGNS): Varied Approaches, Common Principles. Research on Improving Systems of Education (RISE), 2020. http://dx.doi.org/10.35489/bsg-rise-ri_2020/022.

Full text

Abstract:

In this Insight Note, we present a set of principles shared by varied approaches that have all succeeded in improving foundational learning in developing countries. These approaches were not explicitly designed with this list of principles in mind; rather, the principles emerged through analysis and synthesis of successful approaches. We call such efforts ALIGNS approaches, which stands for Aligning Levels of Instruction with Goals and the Needs of Students. ALIGNS approaches take many forms, ranging from large-scale policy and curricular reforms to in-school or after-school remedial programme

APA, Harvard, Vancouver, ISO, and other styles

3

Children with ASD show intact statistical word learning. ACAMH, 2018. http://dx.doi.org/10.13056/acamh.10588.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Early ASD intervention promotes academic achievement. ACAMH, 2018. http://dx.doi.org/10.13056/acamh.10547.

Full text

Abstract:

Rigorous screening for learning difficulties is required for adolescents with Autism Spectrum Disorder (ASD), as a significant minority of affected individuals with average cognitive skills show academic delays, according to a new study.

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!