NDLI: Boosting for transfer learning

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Quantum clustering algorithms

Learning random walks to rank nodes in graphs

Uncovering shared structures in multiclass classification

Two-view feature generation model for semi-supervised learning

Scalable training of $L^{1}-regularized$ log-linear models

Multiclass core vector machine

The rendezvous algorithm: multiclass semi-supervised learning with Markov random walks

Focused crawling with scalable ordinal regression solvers

Learning distance function by coding similarity

Structural alignment based kernels for protein structure classification

Discriminative learning for differing training and test distributions

Solving multiclass support vector machines with LaRank

Efficiently computing minimax expected-size confidence regions

Multiple instance learning for sparse positive bags

Cluster analysis of heterogeneous rank data

Feature selection in a kernel space

Learning to rank: from pairwise approach to listwise approach

Local similarity discriminant analysis

Direct convex relaxations of sparse SVM

Minimum reference set based feature selection for small sample classifications

Learning to compress images and videos

Magnitude-preserving ranking algorithms

Full regularization path for sparse principal component analysis

Kernel selection forl semi-supervised kernel machines

Boosting for transfer learning

Intractability and clustering with constraints

Information-theoretic metric learning

An integrated approach to feature invention and model construction for drug activity prediction

Percentile optimization in uncertain Markov decision processes with application to efficient exploration

Unsupervised prediction of citation influences

Non-isometric manifold learning: analysis and an algorithm

Hierarchical maximum entropy density estimation

CarpeDiem: an algorithm for the fast evaluation of SSL classifiers

Manifold-adaptive dimension estimation

Combining online and offline knowledge in UCT

Robust non-linear dimensionality reduction using successive 1-dimensional Laplacian Eigenmaps

Gradient boosting for kernelized output spaces

Bayesian actor-critic algorithms

Exponentiated gradient algorithms for log-linear structured prediction

Best of both: a hybridized centroid-medoid clustering heuristic

Recovering temporally rewiring networks: a model-based approach

Efficient inference with cardinality-based clique potentials

Sparse probabilistic classifiers

Supervised clustering of streaming data for email batch detection

A bound on the label complexity of agnostic active learning

Learning nonparametric kernel matrices from pairwise constraints

Parameter learning for relational Bayesian networks

Bayesian compressive sensing and projection optimization

Constructing basis functions from directed graphs for value function approximation

Most likely heteroscedastic Gaussian process regression

Neighbor search with global geometry: a minimax message passing algorithm

A recursive method for discriminative mixture learning

Infinite mixtures of trees

Local dependent components

Statistical predicate invention

Kernelizing PLS, degrees of freedom, and efficient model selection

Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach

On one method of non-diagonal regularization in sparse Bayesian learning

Online kernel PCA with entropic matrix updates

An empirical evaluation of deep architectures on problems with many factors of variation

Hierarchical Gaussian process latent variable models

Learning a meta-level prior for feature relevance from multiple related tasks

Scalable modeling of real graphs using Kronecker multiplication

Support cluster machine

A transductive framework of distance metric learning by spectral dimensionality reduction

Adaptive dimension reduction using discriminant analysis and K-means clustering

Large-scale RLSC learning without agony

A novel orthogonal NMF-based belief compression for POMDPs

A permutation-augmented sampler for DP mixture models

Quadratically gated mixture of experts for incomplete data classification

Trust region Newton methods for large-scale logistic regression

Relational clustering by symmetric convex coding

Discriminant analysis in correlation similarity measure space

Adaptive mesh compression in 3D computer graphics using multiscale manifold learning

Simple, robust, scalable semi-supervised learning via expectation regularization

Automatic shaping and decomposition of reward functions

Asymmetric boosting

Linear and nonlinear generative probabilistic class models for shape contours

Bottom-up learning of Markov logic network structure

Mixtures of hierarchical topics with Pachinko allocation

Three new graphical models for statistical language modelling

Fast and effective kernels for relational learning from texts

Dimensionality reduction and generalization

Unsupervised estimation for noisy-channel models

Revisiting probabilistic models for clustering with pair-wise constraints

Comparisons of sequence labeling algorithms and extensions

Multi-task learning for sequential data via iHMMs and the nested Dirichlet process

Regression on manifolds using kernel dimension reduction

Learning state-action basis functions for hierarchical MDPs

A fast linear separability test by projection of positive points on subspaces

Multi-armed bandit problems with dependent arms

Learning for efficient retrieval of structured data with noisy queries

Analyzing feature generation for value-function approximation

Reinforcement learning by reward-weighted regression for operational space control

Tracking value function dynamics to improve reinforcement learning with piecewise linear function approximation

Self-taught learning: transfer learning from unlabeled data

Online discovery of similarity mappings

More efficiency in multiple kernel learning

Graph clustering with network structure indices

Restricted Boltzmann machines for collaborative filtering

Sample compression bounds for decision trees

Pegasos: Primal Estimated sub-GrAdient SOlver for SVM

A dependence maximization view of clustering

Supervised feature selection via dependence estimation

Sparse eigen methods by D.C. programming

Learning to solve game trees

Robust mixtures in the presence of measurement errors

A kernel-based causal learning algorithm

Piecewise pseudolikelihood for efficient training of conditional random fields

On the role of tracking in stationary environments

Cross-domain transfer for reinforcement learning

Incremental Bayesian networks for structure prediction

Classifying matrices with a spectral regularization

Approximate maximum margin algorithms with rules controlled by the number of mistakes

Simpler core vector machines with enclosing balls

Entire regularization paths for graph data

Discriminative Gaussian process latent variable model for classification

Experimental perspectives on learning from imbalanced data

Learning from interpretations: a rooted kernel for ordered hypergraphs

A kernel path algorithm for support vector machines

Dirichlet aggregation: unsupervised learning towards an optimal metric for proportional data

Transductive regression piloted by inter-manifold relations

Multifactor Gaussian process models for style-content separation

Hybrid huberized support vector machines for microarray classification

On learning with dissimilarity functions

Winnowing subspaces

What is decreased by the max-sum arc consistency algorithm?

Multi-task reinforcement learning: a hierarchical Bayesian approach

Beamforming using the relevance vector machine

Learning to combine distances for complex representations

Local learning projections

On learning linear ranking functions for beam search

Modeling changing dependency structure in multivariate time series

The matrix stick-breaking process for flexible multi-task learning

Map building without localization by dimensionality reduction techniques

Asymptotic Bayesian generalization error when training and test distributions are different

Least squares linear discriminant analysis

Discriminant kernel and regularization parameter learning via semidefinite programming

Robust multi-task learning with t-processes

On the value of pairwise constraints in classification and consistency

Maximum margin clustering made practical

Nonlinear independent component analysis with minimal nonlinear distortion

Optimal dimensionality of metric space for classification

Conditional random fields for multi-agent reinforcement learning

Spectral feature selection for supervised and unsupervised learning

Spectral clustering and transductive learning with multiple views

On the relation between multi-instance learning and semi-supervised learning

Dynamic hierarchical Markov random fields and their application to web data extraction

Transductive support vector machines for structured variables

Multiclass multiple kernel learning

Boosting for transfer learning

Content Provider	ACM Digital Library
Author	Dai, Wenyuan Yu, Yong Yang, Qiang Xue, Gui-Rong
Abstract	Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain. Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a novel transfer learning framework called TrAdaBoost, which extends boosting-based learning algorithms (Freund & Schapire, 1997). TrAdaBoost allows users to utilize a small amount of newly labeled data to leverage the old data to construct a high-quality classification model for the new data. We show that this method can allow us to learn an accurate model using only a tiny amount of new data and a large amount of old data, even when the new data are not sufficient to train a model alone. We show that TrAdaBoost allows knowledge to be effectively transferred from the old data to the new. The effectiveness of our algorithm is analyzed theoretically and empirically to show that our iterative algorithm can converge well to an accurate model.
Starting Page	193
Ending Page	200
Page Count	8
File Format	PDF
ISBN	9781595937933
DOI	10.1145/1273496.1273521
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2007-06-20
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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