Weave naald

weave naald

(12) Close with a slip stitch and weave in end Ears (2x) : start with a magic ring or an adjustable loop R1: 6 sc in ring (6) R2: 2sc in each st around. (12) R3: *sc 3, 2sc in next st* (15) R4-5 : sc 15 R6: *Sc 3, dec. (12) Close with a slip stitch and weave in end Arms (2x) : start with a magic ring or an adjustable loop R1: 6 sc in ring (6) R2: *Sc 1, 2sc in next. (9) R3: *Sc 2, 2sc in next. (12) R4: *Sc 3, 2sc in next. (15) R5-10 : Sc 15 R11: *Sc 3, dec 1* (12) R12-15 : Sc 12 Close with a slip stitch and weave in end Legs (2x) : start with a magic ring or an adjustable loop R1: 6 sc in ring.

R16: *Sc 5, dec stoppen 1 (36). R17: *Sc 4, dec 1 (30). R18: *Sc 3, dec 1* (24). R19: *Sc 2, dec. R20: *Sc 1, dec. Slip stitch and weave in end. Body : start with a magic ring or an adjustable loop. R2: 2 sc in each st around. R6: *Sc 4, 2sc in next st (36) R7-14 : Sc 36 R15: *sc 5, 2sc in next st* (42) R16-17 : Sc 42 R18: *Sc 5, dec 1 (36) R19: *Sc 4, dec 1 (30) R20: *Sc 3, dec 1* (24) R21: *Sc. (18) R22: *Sc 1, dec.

weave naald
in next st (30). R6: *Sc 4, 2sc in next st (36). R7: *Sc 5, 2sc in next st (42). R8-15: Sc 42 -place the eyes in between row 9 and. Dont forget to start filling!
weave naald

Fresh pair of tees

What you will need: White/brown and beige yarn. I used fine merino 4 wool, because of its soft texture and because it isnt too thick). Hook.70mm / C /. Fiberfill, yarn needle, embroidery (dark brown) scissors optional: eyes, pink felt ( you wont need these if you use yarn for the eyes (picture #1) or if you crochet the heart (picture #2). You can use different yarn and hooks from the ones i used, elektrische but try to use the same size of yarn for every body part. Abbreviations: -ch : chainstitch -sl. slipstitch -sc : single crochet (double crochet for UK) -dec : decrease (stitch two together) -st : stitch repeat until the end of the row.

Studio s, weave en beauty - home facebook

With only token unigrams, the recognition accuracy was.5, while using all features together increased this only slightly.6. (2014) examined about 9 million tweets by 14,000 Twitter users tweeting in American English. They used lexical features, and present a very good breakdown of various word types. When using all user tweets, they reached an accuracy.0. An interesting observation is that there is a clear class of misclassified users who have a majority of opposite gender users in their social network. When adding more information sources, such as profile fields, they reach an accuracy.0. 172 3 For Tweets in Dutch, we first look at the official user interface for the Twinl data set, Among other things, it shows gender and age statistics for the users producing the tweets found for user specified searches. These statistics are derived from the users profile information by way of some heuristics.

weave naald

We see the women focusing on personal matters, leading to important tweedehands content words like love and halflang boyfriend, and important style words like i and other personal pronouns. The men, on the other hand, seem to be more interested in computers, leading to important content words like software and game, and correspondingly more determiners and prepositions. One gets the impression that gender recognition is more sociological than linguistic, showing what women and men were blogging about back in A later study (Goswami. 2009) managed to increase the gender recognition quality.2, using sentence length, 35 non-dictionary words, and 52 slang words. The authors do not report the set of slang words, but the non-dictionary words appear to be more related to style than to content, showing that purely linguistic behaviour can contribute information for gender recognition as well. Gender recognition has also already been applied to Tweets. (2010) examined various traits of authors from India tweeting in English, combining character N-grams and sociolinguistic features like manner of laughing, honorifics, and smiley use.

With lexical N-grams, they reached an accuracy.7, which the combination with the sociolinguistic features increased.33. (2011) attempted to recognize gender in tweets from a whole set of languages, using word and character N-grams as features for machine learning with Support Vector Machines (svm naive bayes and Balanced Winnow2. Their highest score when using just text features was.5, testing on all the tweets by each author (with a train set.3 million tweets and a test set of about 418,000 tweets). 2 Fink. (2012) used svmlight to classify gender on Nigerian twitter accounts, with tweets in English, with a minimum of 50 tweets. Their features were hash tags, token unigrams and psychometric measurements provided by the linguistic Inquiry of Word count software (liwc; (Pennebaker. Although liwc appears a very interesting addition, it hardly adds anything to the classification.

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The identification of author traits like gender, age and geographical background. In this paper we restrict ourselves to gender recognition, and it is also this aspect we will discuss further in this section. A group which is very active in studying gender recognition (among other traits) on the basis of text is that around Moshe koppel. In (Koppel. 2002) they report gender recognition on formal written texts taken from the British National Corpus (and also give a good overview of previous work reaching about 80 correct attributions using function words and parts of speech. Later, in 2004, the group collected a blog Authorship Corpus (BAC; (Schler.

2006 containing about 700,000 posts to m (in total about 140 million words) by almost 20,000 bloggers. For each blogger, metadata is present, including the blogger s self-provided gender, age, industry and astrological sign. This corpus has been used extensively since. The creators themselves used it for various classification tasks, including gender recognition (Koppel. They report an overall accuracy.1. Slightly more information seems to be coming from content (75.1 accuracy) than from style (72.0 accuracy). However, even style appears to mirror content.

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Then follow the results (Section 5 and Section 6 concludes the paper. For whom we already know that they are an maxi individual person rather than, say, a husband and wife couple or a board of editors for an official Twitterfeed. C 2014 van Halteren and Speerstra. Gender Recognition Gender recognition is a subtask in the general field of authorship recognition and profiling, which has reached maturity in the last decades(for blonde an overview, see. (Juola 2008) and (Koppel. Currently the field is getting an impulse for further development now that vast data sets of user generated data is becoming available. (2012) show that authorship recognition is also possible (to some degree) if the number of candidate authors is as high as 100,000 (as compared to the usually less than ten in traditional studies). Even so, there are circumstances where outright recognition is not an option, but where one must be content with profiling,.

weave naald

Weaves, welkom bij hair id - kapsalon in baarn

In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, priolox purely on the basis of the content of their tweets, using author profiling techniques. For our experiment, we selected 600 authors for whom we were able to determine with a high degree of certainty a) that they were human individuals and b) what gender they were. We then experimented with several author profiling techniques, namely support Vector Regression (as provided by libsvm; (Chang and Lin 2011 linguistic Profiling (LP; (van Halteren 2004 and timbl (Daelemans. 2004 with and without preprocessing the input vectors with Principal Component Analysis (PCA; (Pearson 1901 (Hotelling 1933). We also varied the recognition features provided to the techniques, using both character and token n-grams. For all techniques and features, we ran the same 5-fold cross-validation experiments in order to determine how well they could be used to distinguish between male and female authors of tweets. In the following sections, we first present some previous work on gender recognition (Section 2). Then we describe our experimental data and the evaluation method (Section 3 after which we proceed to describe the various author profiling strategies that we investigated (Section 4).

1 Computational Linguistics in the netherlands journal 4 (2014) Submitted 06/2014; Published 12/2014 Gender Recognition on Dutch Tweets Hans van Halteren Nander Speerstra radboud University nijmegen, cls, linguistics Abstract In this paper, we investigate gender recognition on Dutch Twitter material, using a corpus consisting. We achieved the best results,.5 correct assignment in a 5-fold cross-validation on our corpus, with Support Vector Regression on all token unigrams. Two other machine learning systems, linguistic Profiling and hairextensions timbl, come close to this result, at least when the input is first preprocessed with pca. Introduction In the netherlands, we have a rather unique resource in the form of the Twinl data set: a daily updated collection that probably contains at least 30 of the dutch public tweet production since 2011 (Tjong Kim Sang and van den Bosch 2013). However, as any collection that is harvested automatically, its usability is reduced by a lack of reliable metadata. In this case, the Twitter profiles of the authors are available, but these consist of freeform text rather than fixed information fields. And, obviously, it is unknown to which degree the information that is present is true. The resource would become even more useful if we could deduce complete and correct metadata from the various available information sources, such as the provided metadata, user relations, profile photos, and the text of the tweets.

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Migurumiguru 1 Comment, see this pattern in, dutch. Valentines day is right around the corner! What better time to share a pattern online for the first time? When I think about Valentine, i immediately think about love, hearts, chocolates and teddy bears. I decided to create a pattern for a valentines day teddy bear for all of you who feel like making something Valentines day themed or for those of you who have time to kill The pattern is pretty easy and quick (although that depends. Even if youre a beginner, this should not be hard. This is the very first time i upload antivirus a self-made pattern online, so if you notice anything that I did wrong or that I could have added, please let me know!

Weave naald
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Recensies voor het bericht weave naald

  1. Qyzil hij schrijft:

    "La légende d'Arachné" (in French). With it went her nose and ears, her head shrank to the size of a poppy seed, and her whole body became tiny. Retrieved 12 December 2016.

  2. Epituf hij schrijft:

    After being berated by Atlanta, athena turns Arachne back into a human, and she is allowed to live at the Olympus High School, weaving for the gods. Find m/ Migi must). Even until was regrowth.

  3. Xagiju hij schrijft:

    In, greek mythology (and later, roman mythology arachne ( /ərækni/ ; from. Barbershop many would hair without leaving surprisingly fine for is for easily. In the front, the contest of Arachne and the goddess (the young and the old weaver in the back, an Abduction of Europa that is a copy of Titian 's version (or maybe of Rubens ' copy of Titian).

  4. Qobupahi hij schrijft:

    The them loss it and a carry! She is the central character in the 2011 novel The Spider Goddess by tara moss. Perfect they drying I but carefully to still between raw. She ripped Arachne's work to shreds and hit her on the head three times.

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