From N to N+1:multiclass Transfer Incremental Learning Code Analysis (1)

First, we recommend you read the article: from N to N+1:multiclass Transfer Incremental Learning

Core thought is transfer Incremental learning

In the traditional machine learning hypothesis the training data and the predictive data obey the same data distribution. In many cases, however, the assumption of this distribution is not satisfied. Situations that do not satisfy the same distribution assumptions often occur when training data expires, and it is expensive to label new data. Therefore, in a large number of different distributions of outdated training data, completely discard these outdated data will be very wasteful, in this case, migration learning is very important.

The goal of migration learning is to use the knowledge learned from one environment to help learning tasks in a new environment, so migration learning does not assume the same distribution as traditional machine learning. To cite a popular example, a chess-playing person can learn to play more easily, a person who knows the table can more easily know the chair;

The schematic is as follows: Transfer Learning's task is to detect the puppy, and the system has learned several animals (kittens, horses). Learn the n+1 categories from N categories.

Code components:

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 Li b/tl_baselines/--Baseline TL Algorithm implementation files LIB/GENERICCLASSIFIER.M--ABS                   Tract 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 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 Pm TSVM (Tabula Rasa) (Compatibe with generic evaluation framework) LIB/MULTISOURCETRADABOOST.M--Interface to Multi Sourcetradaboost (Compatibe with generic evaluation framework) LIB/MULTIPLE.M--The multiple Algorit HM Implementation NPLUSONEBENCHMARK.M--main experiment file; Preamble contains its description