From N to N+1: Multiclass Transfer Incremental Learning 代码分析（1）

首先推荐大家看文章：From N to N+1: Multiclass Transfer Incremental Learning

核心细想就是Transfer Incremental Learning

原理图如下：Transfer learning的任务就是检测出小狗，并且系统已经学习到了几种动物（小猫、马）。从有n个类别学习第n+1个类别。

代码组成部分：

Contents
--------

data/                               -- demo data
tmp/                                -- temporary files (e.g. source classifiers)
lib/                                -- algorithm implementations
lib/util                            -- utilities
lib/dogma                           -- parts from DOGMA library
lib/mktl                            -- Multi-Kernel Transfer Learning implementation files
lib/multikt                         -- MultiKT implementation files
lib/tl_baselines/                   -- baseline TL algorithm implementation files
lib/GenericClassifier.m             -- abstract base class for classifiers (kernel computation and generic evaluation routines)
lib/HyperSearch.m                   -- hyperparameter grid search utility class
lib/MulticlassOneVsRest.m           -- multiclass OVA classifier, where binary classfiers can be plugged in
lib/MulticlassRLS.m                 -- multiclass LSSVM classifier
lib/SimpleNplusOne.m                -- Source+1 baseline implementation
lib/SourcePlusOneHingeL.m           -- Source+1 (hinge) baseline implementation
lib/MTKL.m                          -- interface to MKTL (compatibe with generic evaluation framework)
lib/MultiKT.m                       -- interface to MultiKT (compatibe with generic evaluation framework)
lib/PmtSvm.m                        -- interface to PmtSvm (Tabula Rasa) (compatibe with generic evaluation framework)
lib/MultisourceTrAdaBoost.m         -- interface to MultisourceTrAdaBoost (compatibe with generic evaluation framework)
lib/MULTIpLE.m                      -- The MULTIpLE algorithm implementation
NplusoneBenchmark.m                 -- main experiment file; preamble contains its description