We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period.
{"title":"Autonomous shaping: knowledge transfer in reinforcement learning","authors":"G. Konidaris, A. Barto","doi":"10.1145/1143844.1143906","DOIUrl":"https://doi.org/10.1145/1143844.1143906","url":null,"abstract":"We introduce the use of learned shaping rewards in reinforcement learning tasks, where an agent uses prior experience on a sequence of tasks to learn a portable predictor that estimates intermediate rewards, resulting in accelerated learning in later tasks that are related but distinct. Such agents can be trained on a sequence of relatively easy tasks in order to develop a more informative measure of reward that can be transferred to improve performance on more difficult tasks without requiring a hand coded shaping function. We use a rod positioning task to show that this significantly improves performance even after a very brief training period.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129912713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. This kernel is also shown to enhance retrieval based on data similarity. Specifically, we describe KernelBoost - a boosting algorithm which computes a kernel function as a combination of 'weak' space partitions. The kernel learning method naturally incorporates domain knowledge in the form of unlabeled data (i.e. in a semi-supervised or transductive settings), and also in the form of labeled samples from relevant related problems (i.e. in a learning-to-learn scenario). The latter goal is accomplished by learning a single kernel function for all classes. We show comparative evaluations of our method on datasets from the UCI repository. We demonstrate performance enhancement on two challenging tasks: digit classification with kernel SVM, and facial image retrieval based on image similarity as measured by the learnt kernel.
{"title":"Learning a kernel function for classification with small training samples","authors":"T. Hertz, Aharon Bar-Hillel, D. Weinshall","doi":"10.1145/1143844.1143895","DOIUrl":"https://doi.org/10.1145/1143844.1143895","url":null,"abstract":"When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. This kernel is also shown to enhance retrieval based on data similarity. Specifically, we describe KernelBoost - a boosting algorithm which computes a kernel function as a combination of 'weak' space partitions. The kernel learning method naturally incorporates domain knowledge in the form of unlabeled data (i.e. in a semi-supervised or transductive settings), and also in the form of labeled samples from relevant related problems (i.e. in a learning-to-learn scenario). The latter goal is accomplished by learning a single kernel function for all classes. We show comparative evaluations of our method on datasets from the UCI repository. We demonstrate performance enhancement on two challenging tasks: digit classification with kernel SVM, and facial image retrieval based on image similarity as measured by the learnt kernel.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126872364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Darrin P. Lewis, T. Jebara, William Stafford Noble
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several methods have been proposed for combining kernels from heterogeneous data sources. However, all of these methods produce stationary combinations; i.e., the relative weights of the various kernels do not vary among input examples. This article proposes a method for combining multiple kernels in a nonstationary fashion. The approach uses a large-margin latent-variable generative model within the maximum entropy discrimination (MED) framework. Latent parameter estimation is rendered tractable by variational bounds and an iterative optimization procedure. The classifier we use is a log-ratio of Gaussian mixtures, in which each component is implicitly mapped via a Mercer kernel function. We show that the support vector machine is a special case of this model. In this approach, discriminative parameter estimation is feasible via a fast sequential minimal optimization algorithm. Empirical results are presented on synthetic data, several benchmarks, and on a protein function annotation task.
{"title":"Nonstationary kernel combination","authors":"Darrin P. Lewis, T. Jebara, William Stafford Noble","doi":"10.1145/1143844.1143914","DOIUrl":"https://doi.org/10.1145/1143844.1143914","url":null,"abstract":"The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several methods have been proposed for combining kernels from heterogeneous data sources. However, all of these methods produce stationary combinations; i.e., the relative weights of the various kernels do not vary among input examples. This article proposes a method for combining multiple kernels in a nonstationary fashion. The approach uses a large-margin latent-variable generative model within the maximum entropy discrimination (MED) framework. Latent parameter estimation is rendered tractable by variational bounds and an iterative optimization procedure. The classifier we use is a log-ratio of Gaussian mixtures, in which each component is implicitly mapped via a Mercer kernel function. We show that the support vector machine is a special case of this model. In this approach, discriminative parameter estimation is feasible via a fast sequential minimal optimization algorithm. Empirical results are presented on synthetic data, several benchmarks, and on a protein function annotation task.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134365092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recently, a very appealing approach was proposed to compute the entire solution path for support vector classification (SVC) with very low extra computational cost. This approach was later extended to a support vector regression (SVR) model called ε-SVR. However, the method requires that the error parameter ε be set a priori, which is only possible if the desired accuracy of the approximation can be specified in advance. In this paper, we show that the solution path for ε-SVR is also piecewise linear with respect to ε. We further propose an efficient algorithm for exploring the two-dimensional solution space defined by the regularization and error parameters. As opposed to the algorithm for SVC, our proposed algorithm for ε-SVR initializes the number of support vectors to zero and then increases it gradually as the algorithm proceeds. As such, a good regression function possessing the sparseness property can be obtained after only a few iterations.
{"title":"Two-dimensional solution path for support vector regression","authors":"G. Wang, D. Yeung, F. Lochovsky","doi":"10.1145/1143844.1143969","DOIUrl":"https://doi.org/10.1145/1143844.1143969","url":null,"abstract":"Recently, a very appealing approach was proposed to compute the entire solution path for support vector classification (SVC) with very low extra computational cost. This approach was later extended to a support vector regression (SVR) model called ε-SVR. However, the method requires that the error parameter ε be set a priori, which is only possible if the desired accuracy of the approximation can be specified in advance. In this paper, we show that the solution path for ε-SVR is also piecewise linear with respect to ε. We further propose an efficient algorithm for exploring the two-dimensional solution space defined by the regularization and error parameters. As opposed to the algorithm for SVC, our proposed algorithm for ε-SVR initializes the number of support vectors to zero and then increases it gradually as the algorithm proceeds. As such, a good regression function possessing the sparseness property can be obtained after only a few iterations.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131450524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Feature selection is often applied to high-dimensional data prior to classification learning. Using the same training dataset in both selection and learning can result in so-called feature subset selection bias. This bias putatively can exacerbate data over-fitting and negatively affect classification performance. However, in current practice separate datasets are seldom employed for selection and learning, because dividing the training data into two datasets for feature selection and classifier learning respectively reduces the amount of data that can be used in either task. This work attempts to address this dilemma. We formalize selection bias for classification learning, analyze its statistical properties, and study factors that affect selection bias, as well as how the bias impacts classification learning via various experiments. This research endeavors to provide illustration and explanation why the bias may not cause negative impact in classification as much as expected in regression.
{"title":"Feature subset selection bias for classification learning","authors":"Surendra K. Singhi, Huan Liu","doi":"10.1145/1143844.1143951","DOIUrl":"https://doi.org/10.1145/1143844.1143951","url":null,"abstract":"Feature selection is often applied to high-dimensional data prior to classification learning. Using the same training dataset in both selection and learning can result in so-called feature subset selection bias. This bias putatively can exacerbate data over-fitting and negatively affect classification performance. However, in current practice separate datasets are seldom employed for selection and learning, because dividing the training data into two datasets for feature selection and classifier learning respectively reduces the amount of data that can be used in either task. This work attempts to address this dilemma. We formalize selection bias for classification learning, analyze its statistical properties, and study factors that affect selection bias, as well as how the bias impacts classification learning via various experiments. This research endeavors to provide illustration and explanation why the bias may not cause negative impact in classification as much as expected in regression.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130285109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes new approaches to rank individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. We propose an exponential model to solve such problems. To estimate individual rankings through the proposed model we introduce two convex minimization formulas with easy and efficient solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed model.
{"title":"Ranking individuals by group comparisons","authors":"Tzu-Kuo Huang, Chih-Jen Lin, R. C. Weng","doi":"10.1145/1143844.1143898","DOIUrl":"https://doi.org/10.1145/1143844.1143898","url":null,"abstract":"This paper proposes new approaches to rank individuals from their group competition results. Many real-world problems are of this type. For example, ranking players from team games is important in some sports. We propose an exponential model to solve such problems. To estimate individual rankings through the proposed model we introduce two convex minimization formulas with easy and efficient solution procedures. Experiments on real bridge records and multi-class classification demonstrate the viability of the proposed model.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125218261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenmaps, and maximum variance unfolding. We discuss the duality theory for the maximum variance unfolding problem, and show that other methods are directly related to either its primal formulation or its dual formulation, or can be interpreted from the optimality conditions. This duality framework reveals close connections between these seemingly quite different algorithms. In particular, it resolves the myth about these methods in using either the top eigenvectors of a dense matrix, or the bottom eigenvectors of a sparse matrix --- these two eigenspaces are exactly aligned at primal-dual optimality.
{"title":"A duality view of spectral methods for dimensionality reduction","authors":"Lin Xiao, Jun Sun, Stephen P. Boyd","doi":"10.1145/1143844.1143975","DOIUrl":"https://doi.org/10.1145/1143844.1143975","url":null,"abstract":"We present a unified duality view of several recently emerged spectral methods for nonlinear dimensionality reduction, including Isomap, locally linear embedding, Laplacian eigenmaps, and maximum variance unfolding. We discuss the duality theory for the maximum variance unfolding problem, and show that other methods are directly related to either its primal formulation or its dual formulation, or can be interpreted from the optimality conditions. This duality framework reveals close connections between these seemingly quite different algorithms. In particular, it resolves the myth about these methods in using either the top eigenvectors of a dense matrix, or the bottom eigenvectors of a sparse matrix --- these two eigenspaces are exactly aligned at primal-dual optimality.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115402913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.
{"title":"Local Fisher discriminant analysis for supervised dimensionality reduction","authors":"Masashi Sugiyama","doi":"10.1145/1143844.1143958","DOIUrl":"https://doi.org/10.1145/1143844.1143958","url":null,"abstract":"Dimensionality reduction is one of the important preprocessing steps in high-dimensional data analysis. In this paper, we consider the supervised dimensionality reduction problem where samples are accompanied with class labels. Traditional Fisher discriminant analysis is a popular and powerful method for this purpose. However, it tends to give undesired results if samples in some class form several separate clusters, i.e., multimodal. In this paper, we propose a new dimensionality reduction method called local Fisher discriminant analysis (LFDA), which is a localized variant of Fisher discriminant analysis. LFDA takes local structure of the data into account so the multimodal data can be embedded appropriately. We also show that LFDA can be extended to non-linear dimensionality reduction scenarios by the kernel trick.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115786564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.
{"title":"The relationship between Precision-Recall and ROC curves","authors":"Jesse Davis, Mark H. Goadrich","doi":"10.1145/1143844.1143874","DOIUrl":"https://doi.org/10.1145/1143844.1143874","url":null,"abstract":"Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116622720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends on the choice of the kernel; in this paper, we consider the problem of finding the optimal kernel, over a given convex set of kernels. We show that this optimal kernel selection problem can be reformulated as a tractable convex optimization problem which interior-point methods can solve globally and efficiently. The kernel selection method is demonstrated with some UCI machine learning benchmark examples.
{"title":"Optimal kernel selection in Kernel Fisher discriminant analysis","authors":"Seung-Jean Kim, A. Magnani, Stephen P. Boyd","doi":"10.1145/1143844.1143903","DOIUrl":"https://doi.org/10.1145/1143844.1143903","url":null,"abstract":"In Kernel Fisher discriminant analysis (KFDA), we carry out Fisher linear discriminant analysis in a high dimensional feature space defined implicitly by a kernel. The performance of KFDA depends on the choice of the kernel; in this paper, we consider the problem of finding the optimal kernel, over a given convex set of kernels. We show that this optimal kernel selection problem can be reformulated as a tractable convex optimization problem which interior-point methods can solve globally and efficiently. The kernel selection method is demonstrated with some UCI machine learning benchmark examples.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128244721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：