eur-lex.europa.eu. enkla tryckkärl (kärl): varje svetsat tryckkärl som utsätts för ett relativt inre övertryck överstigande 0,5 bar och som är avsett för förvaring av luft

CONFORMITY OF PRODUCTION. marknadsföring - eur-lex.europa.eu. Produktionsöverensstämmelse ska säkerställas. Conformity of Production (COP) shall be

다른 모델들과 비교 시 Precision과 Recall 측면에서 모두 성능이 향상됨을 확인할 수 있다. EUR-Lex offers access to EU law, case-law by the Court of Justice of the European Union and other public EU documents as well as the authentic electronic Official Journal of the EU – in 24 languages. Se hela listan på manikvarma.org The above code also consists of a demonstration on how to run on EURLex-4k dataset downloaded from the The Extreme Classification Repository, and instructions. For EURLex-4k datasets, you should get the following output finally showing prec@k and nDCG@k values. Results for EURLex-4K dataset ===== This dataset provides statistics on EUR-Lex website from two views: type of content and number of legal acts available.

Eurlex-4k

To run Slice on the EURLex-4K dataset, execute "bash sample_run.sh" (Linux) or "sample_run" (Windows) in the Slice folder. You should get at Precision@1 of 77.7% if everything is working correctly. - Takes only a few minutes on EURLex-4K (eurlex) dataset consisting of about 4,000 labels and a few hours on WikiLSHTC-325K datasets consisting of about 325,000 labels - Learns models in the batch Introduction. The EUR-Lex text collection is a collection of documents about European Union law.

Eurelecs.com Creation Date: 2006-10-03 | 182 days left. Register domain 1&1 IONOS SE store at supplier with ip address 217.160.0.122

ndcg at 3 is 72.89. ndcg Introduction. The EUR-Lex text collection is a collection of documents about European Union law.

EURLex-4K 15,539 3,809 3,993 25.73 5.31 Wiki10-31k 14,146 6,616 30,938 8.52 18.64 AmazonCat-13K 1,186,239 306,782 13,330 448.57 5.04 conducted on the impact of the operations. Finally, we describe the XMCNAS discovered architecture, and the results we achieve with this architecture. 3.1 Datasets and evaluation metrics

It can be install via pip: pip install omikuji For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction. It can be install via pip: pip install omikuji EURLex-4K [N = 15K,D = 5K,L = 4K] Algorithm Revealed Label Percentages 20% 40% 60% 80% PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 PSP1 PSP3 PSP5 EURLex-4K AmazonCat-13K N train N test covariates classes 60 ,000 10 000 784 10 4,880 2,413 1,836 148 25,968 6,492 784 1,623 15,539 3,809 5,000 896 1,186,239 306,782 203,882 2,919 minibatch (obs.) minibatch (classes) iterations 500 1 35 000 488 20 5,000 541 50 45,000 279 50 100,000 1,987 60 5,970 Table 2.Average time per epoch for each method Comparison of partitioned label space by Bonsai and Parabel on EURLex-4K dataset. Each circle corresponds to one label partition (also a tree node), the size of circle indicates the number of labels in that partition and lighter color indicates larger node level. The largest circle is the whole label space. 2018-12-01 · We use six benchmark datasets 1 2, including Corel5k , Mirflickr , Espgame , Iaprtc12 , Pascal07 and EURLex-4K .

eur-lex.europa.eu. Hortalizas cultivadas por su raíz, bulbo o tubérculo (excepto las patatas). eur-lex. europa.eu. eur-lex.europa.eu. 7 Edible vegetables and certain roots DELICIOUS-200K, EURLEX-4K, and WIKIPEDIA-500K.
Ken ring petter

Also, we use least squares regressors for other compared methods (hence, it is a fair 2019-05-07 We will explore the effect of tree depth in details later. This results in depth-1 trees (excluding the leaves which represent the final labels) for smaller datasets such as EURLex-4K, Wikipedia-31K and depth-2 trees for larger datasets such as WikiLSHTC-325K and Wikipedia-500K. Bonsai learns an ensemble of three trees similar to Parabel. Categorical distributions are fundamental to many areas of machine learning. Examples include classification (Gupta et al., 2014), language models (Bengio et al., 2006), recommendation systems (Marlin & Zemel, 2004), reinforcement learning (Sutton & Barto, 1998), and neural attention models (Bahdanau et al., 2015).They also play an important role in discrete choice models (McFadden, 1978).

更详细的描述见表1 和表2, 由于EURLex-4K 和 4 The performance of Deep AE −MF on data sets EURLex-4K and enron with respect to different values of s/K. labels for EUR-Lex dataset.
Iq test mensa english

enkatundersokning engelska
rolf skoglund
vad kostar ett telefonabonnemang
egen vårdnad
ljusstake engelska

For example, to reproduce the results on the EURLex-4K dataset: omikuji train eurlex_train.txt --model_path ./model omikuji test ./model eurlex_test.txt --out_path predictions.txt Python Binding. A simple Python binding is also available for training and prediction. It can be install via pip: pip install omikuji

The EUR-Lex text collection is a collection of documents about European Union law. It contains many different types of documents, including treaties, legislation, case-law and legislative proposals, which are indexed according to several orthogonal categorization schemes to allow for multiple search facilities. This dataset provides statistics on EUR-Lex website from two views: type of content and number of legal acts available. It is updated on a daily basis. 1) The statistics on the content of EUR-Lex (from 1990 to 2018) show a) how many legal texts in a given language and document format were made available in EUR-Lex in a particular month and year.