train_class_svm — MV `classify` op

• Data kinds: matrix → table

• Call: import fullseye as fs; fs.ledger.train_class_svm(features, labels, C=1.0, epochs=300, lr=0.01) (to call the implementation directly, import final_genuine2; final_genuine2.train_class_svm(features, labels, C=1.0, epochs=300, lr=0.01); from the registry, opsmv.get("train_class_svm"))

• HALCON equivalent: train_class_svm (the HALCON reference is a useful guide to its meaning and parameters)

• GPU: none (CPU reference implementation only)

Usage

Train a linear SVM (hinge loss, one-vs-rest).

> The detailed description below is the original text — the summary and the headings are translated.

特徴は標準化してから学習し、mu / sd をモデルに残す(classify_image_class_svm が使う)。one-vs-rest の線形分離なので、一直線に並んだ 3 クラスの真ん中のような配置は苦手(既知の限界)。空の入力(features が 0 要素)は ValueError。

Detailed usage guide

• machine_vision_classics family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• machine_vision_tour — py -3.11 examples/machine_vision_tour.py

Ops the type connects to (they accept `table` as input)

map_image · rectify_to_world_plane · change_radial_distortion_image · find_ncc_models · run_bg_esti · give_bg_esti · update_bg_esti · classify_class_gmm

Same category (`classify`)

train_class_gmm · classify_class_gmm · evaluate_class_gmm · train_class_knn · classify_class_knn · train_class_mlp · classify_class_mlp · classify_class_svm

*Provenance: final_genuine2.py — MV operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

train_class_svm — MV classify op