In pattern recognition a feature space is an abstract space where each pattern sample is represented as a point in n-dimensional space. Its dimension is determined by the number of features used to describe the patterns. Similar samples are grouped together, which allows the use of density estimation for finding patterns.
aa
- ab
- af
- ak
- als
- am
- an
- ang
- ar
- arc
- as
- ast
- av
- ay
- az
- ba
- bar
- bat_smg
- bcl
- be
- be_x_old
- bg
- bh
- bi
- bm
- bn
- bo
- bpy
- br
- bs
- bug
- bxr
- ca
- cbk_zam
- cdo
- ce
- ceb
- ch
- cho
- chr
- chy
- co
- cr
- crh
- cs
- csb
- cu
- cv
- cy
- da
- de
- diq
- dsb
- dv
- dz
- ee
- el
- eml
- en
- eo
- es
- et
- eu
- ext
- fa
- ff
- fi
- fiu_vro
- fj
- fo
- fr
- frp
- fur
- fy
- ga
- gan
- gd
- gl
- glk
- gn
- got
- gu
- gv
- ha
- hak
- haw
- he
- hi
- hif
- ho
- hr
- hsb
- ht
- hu
- hy
- hz
- ia
- id
- ie
- ig
- ii
- ik
- ilo
- io
- is
- it
- iu
- ja
- jbo
- jv
- ka
- kaa
- kab
- kg
- ki
- kj
- kk
- kl
- km
- kn
- ko
- kr
- ks
- ksh
- ku
- kv
- kw
- ky
- la
- lad
- lb
- lbe
- lg
- li
- lij
- lmo
- ln
- lo
- lt
- lv
- map_bms
- mdf
- mg
- mh
- mi
- mk
- ml
- mn
- mo
- mr
- mt
- mus
- my
- myv
- mzn
- na
- nah
- nap
- nds
- nds_nl
- ne
- new
- ng
- nl
- nn
- no
- nov
- nrm
- nv
- ny
- oc
- om
- or
- os
- pa
- pag
- pam
- pap
- pdc
- pi
- pih
- pl
- pms
- ps
- pt
- qu
- quality
- rm
- rmy
- rn
- ro
- roa_rup
- roa_tara
- ru
- rw
- sa
- sah
- sc
- scn
- sco
- sd
- se
- sg
- sh
- si
- simple
- sk
- sl
- sm
- sn
- so
- sr
- srn
- ss
- st
- stq
- su
- sv
- sw
- szl
- ta
- te
- tet
- tg
- th
- ti
- tk
- tl
- tlh
- tn
- to
- tpi
- tr
- ts
- tt
- tum
- tw
- ty
- udm
- ug
- uk
- ur
- uz
- ve
- vec
- vi
- vls
- vo
- wa
- war
- wo
- wuu
- xal
- xh
- yi
- yo
- za
- zea
- zh
- zh_classical
- zh_min_nan
- zh_yue
- zu
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