svm-train(1)

💖 svm-train — the girlies are TRAINING 💖

okay so bestie, welcome to svm-train(1) from the User Manuals section, and yes this is the one that makes the magic model file happen ✨

---

✨ NAME ✨

svm-train — train one or more SVM instance(s) on a given data set to produce a model file 🎀

---

🌸 SYNOPSIS 🌸

svm-train [-s svm_type ] [ -t kernel_type ] [ -d degree ] [ -g gamma ] [
-r coef0 ] [ -c cost ] [ -n nu ] [ -p epsilon ] [ -m cachesize ] [ -e
epsilon ] [ -h shrinking ] [ -b probability_estimates ] ] [ -wi weight ]
[ -v n ] [ -q ]

training_set_file [ model_file ]

no fr, that's the whole vibe of it 💅

---

🦋 DESCRIPTION 🦋

so svm-train trains a Support Vector Machine to learn the data you point it at in the training_set_file, and then it produces a model_file to save the results of the learning optimization 🧚

that model is literally your main character moment — you can use it later with svm_predict(1) or with other LIBSVM enabled software. slay 🔥

---

👑 OPTIONS 👑

• -s svm_type — svm_type defaults to 0 and can be any value between 0 and 4, like this:
  • 0 — C-SVC
  • 1 — nu-SVC
  • 2 — one-class SVM
  • 3 — epsilon-SVR
  • 4 — nu-SVR
• -t kernel_type — kernel_type defaults to 2 (Radial Basis Function (RBF) kernel) and can be any value between 0 and 4, as follows:
  • 0 — linear: u.v
  • 1 — polynomial: (gamma*u.v + coef0)^degree
  • 2 — radial basis function: exp(-gamma*|u-v|^2)
  • 3 — sigmoid: tanh(gamma*u.v + coef0)
  • 4 — precomputed kernel (kernel values in training_set_file) --
• -d degree — sets the degree of the kernel function, defaulting to 3 ⭐
• -g gamma — adjusts the gamma in the kernel function (default 1/k) 🌈
• -r coef0 — sets the coef0 (constant offset) in the kernel function (default 0) 💜
• -c cost — sets the parameter C ( cost ) of C-SVC, epsilon-SVR, and nu-SVR (default 1) 💰
• -n nu — sets the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5) 🎀
• -p epsilon — set the epsilon in the loss function of epsilon-SVR (default 0.1) 🍓
• -m cachesize — set the cache memory size to cachesize in MB (default 100) 🧠
• -e epsilon — set the tolerance of termination criterion to epsilon (default 0.001) ⏱️
• -h shrinking — whether to use the shrinking heuristics, 0 or 1 (default 1) 🪄
• -b probability-estimates — probability_estimates is a binary value indicating whether to calculate probability estimates when training the SVC or SVR model. values are 0 or 1 and defaults to 0 for speed 🏎️
• -wi weight — set the parameter C (cost) of class i to weight*C, for C-SVC (default 1) ⚖️
• -v n — set n for n -fold cross validation mode 🔁
• -q — quiet mode; suppress messages to stdout 🤫

---

📁 FILES 📁

training_set_file must be prepared in the following simple sparse training vector format:

<label> <index1>:<value1> <index2>:<value2> . . .

 .

 .

 .

there is one sample per line 📝 each sample consists of a target value (label or regression target) followed by a sparse representation of the input vector. all unmentioned coordinates are assumed to be 0 🫶

for classification, <label> is an integer indicating the class label (multi-class is supported) 💖 for regression, <label> is the target value which can be any real number 📈 for one-class SVM, it's not used so can be any number 🤷♀️

except using precomputed kernels (explained in another section), <index>:<value> gives a feature (attribute) value. <index> is an integer starting from 1 and <value> is a real number.

ok so this part is IMPORTANT bestie: indices must be in an ASCENDING order. do not come at it out of order, it will not eat 💀

---

🌿 ENVIRONMENT 🌿

No environment variables. that's it, nothing to set up, we love a low-maintenance queen 💅

---

🔍 DIAGNOSTICS 🔍

None documented; see Vapnik et al. 📚

---

💀 BUGS 💀

Please report bugs to the Debian BTS. 🐛

---

🫶 AUTHOR 🫶

Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging) — icons, all of them 👑

---

🌈 SEE ALSO 🌈

svm-predict(1), svm-scale(1) — go read those next, iykyk ✨

---

and that's the whole page bestie!! this one comes from the Linux corner of the world, dated MAY 2006 — yeah she's a classic, an OG, and she still serves 🎀💖 no cap, go train something iconic, periodt ✨🦋🌸