1 |
American Sign Language Alphabet Recognition by Extracting Feature from Hand Pose Estimation
|
|
|
|
In: Sensors ; Volume 21 ; Issue 17 (2021)
|
|
BASE
|
|
Show details
|
|
2 |
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
|
|
|
|
In: Multimodal Technologies and Interaction ; Volume 4 ; Issue 1 (2020)
|
|
BASE
|
|
Show details
|
|
3 |
La potenciación descortés del desacuerdo en hablantes españoles e ingleses ; Impolite boosting of disagreement in Spanish and English speakers
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Interactional Metadiscourse In Doctoral Thesis Writing: A Study in Kenya
|
|
|
|
In: Applied Linguistics Research Journal, Vol 4, Iss 4, Pp 100-113 (2020) (2020)
|
|
BASE
|
|
Show details
|
|
5 |
Computing Happiness from Textual Data
|
|
|
|
In: Stats ; Volume 2 ; Issue 3 ; Pages 25-370 (2019)
|
|
BASE
|
|
Show details
|
|
6 |
Arabic-SOS: Segmentation, stemming, and orthography standardization for classical and pre-modern standard Arabic
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Computing Happiness from Textual Data
|
|
|
|
In: 2 ; 3 ; 347 ; 370 (2019)
|
|
BASE
|
|
Show details
|
|
9 |
IRISA at DeFT2017 : classification systems of increasing complexity ; Participation de l'IRISA à DeFT2017 : systèmes de classification de complexité croissante
|
|
|
|
In: DeFT 2017 - Défi Fouille de texte ; https://hal.archives-ouvertes.fr/hal-01643993 ; DeFT 2017 - Défi Fouille de texte, Jun 2017, Orléans, France. pp.1-10 (2017)
|
|
BASE
|
|
Show details
|
|
10 |
The Functions of Narrative Passages in Three Written Online Health Contexts
|
|
|
|
In: Open Linguistics, Vol 2, Iss 1 (2016) (2016)
|
|
BASE
|
|
Show details
|
|
11 |
ОБЗОР МЕТОДОВ И АЛГОРИТМОВ РАЗРЕШЕНИЯ ЛЕКСИЧЕСКОЙ МНОГОЗНАЧНОСТИ: ВВЕДЕНИЕ
|
|
|
|
BASE
|
|
Show details
|
|
12 |
IRISA at DeFT 2015: Supervised and Unsupervised Methods in Sentiment Analysis
|
|
|
|
In: DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015 ; https://hal.archives-ouvertes.fr/hal-01226528 ; DeFT, Défi Fouille de Texte, joint à la conférence TALN 2015, Jun 2015, Caen, France (2015)
|
|
BASE
|
|
Show details
|
|
13 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
A nonparametric Bayesian perspective for machine learning in partially-observed settings
|
|
|
|
BASE
|
|
Show details
|
|
15 |
All cumulative semantic interference is not equal: A test of the Dark Side Model of lexical access
|
|
|
|
BASE
|
|
Show details
|
|
16 |
Sign Language Recognition using Sub-Units
|
|
|
|
In: http://personal.ee.surrey.ac.uk/Personal/R.Bowden/publications/2012/Cooper_JMLR_2012.pdf (2012)
|
|
BASE
|
|
Show details
|
|
17 |
Boosting of fuzzy rules with low quality data
|
|
|
|
In: http://sci2s.ugr.es/publications/ficheros/JMVLSC2011.pdf (2011)
|
|
BASE
|
|
Show details
|
|
18 |
Adasum: an adaptive model for summarization
|
|
|
|
In: http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2008/p901-zhang.pdf (2008)
|
|
Abstract:
Topic representation mismatch is a key problem in topic-oriented summarization for the specified topic is usually too short to understand/interpret. This paper proposes a novel adaptive model for summarization, AdaSum, under the assumption that the summary and the topic representation can be mutually boosted. Ada-Sum aims to simultaneously optimize the topic representation and extract effective summaries. This model employs a mutual boosting process to minimize the topic representation mismatch for base summarizers. Furthermore, a linear combination of base summarizers is proposed to further reduce the topic representation mismatch from the diversity of base summarizers with a general learning framework. We prove that the training process of AdaSum can enhance the performance measure used. Experimental results on DUC 2007 dataset show that AdaSum significantly outperforms the baseline methods for summarization (e.g. MRP, LexRank, and GSPS).
|
|
Keyword:
Ada- Sum; Boosting; Experimentation. Keywords Topic-oriented Summarization; General Terms Algorithms; Performance; Topic Representation
|
|
URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.222.6151 http://www.cs.fiu.edu/%7Elli003/Sum/CIKM/2008/p901-zhang.pdf
|
|
BASE
|
|
Hide details
|
|
19 |
A Multilingual Named Entity Recognition System Using Boosting and C4.5 Decision Tree Learning Algorithms. Discovery Science 2006
|
|
|
|
In: http://www.inf.u-szeged.hu/~rfarkas/ds_lnai.pdf (2006)
|
|
BASE
|
|
Show details
|
|
20 |
The ICSI+ Multilingual Sentence Segmentation System
|
|
|
|
In: DTIC (2006)
|
|
BASE
|
|
Show details
|
|
|
|