Spodaj je zbirka dodatnega gradiva na temo strojnega učenja, predvsem metode analize sentimenta pri preučevanju financ v ekonomiji, torej tremi članki, ki jih bomo obravnavali na seminarju. Vsebuje tudi nekaj druge literature, ki se tukaj običajno navaja.
Spodnja zbirka vključuje sledeče članke (dobite jih s klikom na naslov članka)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media (pp. 30–38). Association for Computational Linguistics.
Antweiler, W. & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59 (3), 1259–1294.
Argamon-Engelson, S. & Dagan, I. (1999). Committee-based sample selection for probabilistic classifiers. Journal of Artificial Intelligence Research, 11, 335–360.
Asur, S. & Huberman, B. A. (2010). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 492–499).
Bifet, A. & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. In Proceedings of the 13th International Conference on Discovery Science (DS) (pp. 1–15). Springer.
Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2 (1), 1–8.
Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.
Chen, R. & Lazer, M. (2011). Sentiment analysis of Twitter feeds for the prediction of stock market movement. CS 229 Machine Learning : Final Project.
Choudhry, R., & Garg, K. (2008). A hybrid machine learning system for stock market forecasting. World Academy of Science, Engineering and Technology, 39(3), 315-318.
Chung, J. E. & Mustafaraj, E. (2011). Can collective sentiment expressed on Twitter predict political elections? In Proceedings of the 25th AAAI Conference on Artificial Intelligence.
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20 (3), 273–297.
Cover, T. & Hart, P. E. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13 (1), 21–27.
Fan, A., & Palaniswami, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 354-359). IEEE.
Flach, P. (2012). Machine learning: The art and science of algorithms that make sense of data. Cambridge University Press.
Galindo, J., & Tamayo, P. (2000). Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications. Computational Economics, 15(1), 107-143.
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12.
González-Ibáñez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2
(pp. 581–586). Association for Computational Linguistics.
Granger, C. W. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: Journal of the Econometric Society, 424–438.
Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (pp. 78–87). ACM.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32.
Jahanbakhsh, K. & Moon, Y. (2014). The predictive power of social media: On the predictability of US presidential elections using Twitter. arXiv preprint arXiv:1407.0622.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (pp. 137–142).
Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - support vector learning (Chap. 11, pp. 169–184). Cambridge, MA: MIT Press.
Joachims, T. (2005). A support vector method for multivariate performance measures. In Proceedings of the 22nd International Conference on Machine Learning (pp. 377–384). ACM.
Joachims, T. (2006). Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 217–226). ACM.
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI) - Volume 2 (pp. 1137–1143).
Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the OMG! In Proceedings of The International Conference on Weblogs and Social Media (ICWSM) (Vol. 11, pp. 538–541).
Kranjc, J., Podpečan, V., & Lavrač, N. (2012). Clowdflows: A cloud based scientific work-flow platform. In Machine Learning and Knowledge Discovery in Databases (pp. 816–819). Springer.
Kranjc, J., Podpečan, V., & Lavrač, N. (2013). Real-time data analysis in ClowdFlows. In Proceedings of The 2013 IEEE International Conference on Big Data (pp. 15–22). IEEE.
Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, M., & Lavrač, N. (2014). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing & Management.
doi:http://dx.doi.org/10.1016/j.ipm.2014.04.001
Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2012). Machine learning in financial crisis prediction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421-436.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2, 627–666.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5 (1), 1–167.
Martineau, J. & Finin, T. (2009). Delta TFIDF: An improved feature space for sentiment analysis. In Proceedings of the Third AAAI Internatonal Conference on Weblogs and Social Media.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal.
Mishne, G. & Glance, N. S. (2006). Predicting movie sales from blogger sentiment. In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs (pp. 155–158).
Mittal, A. & Goel, A. (2012). Stock prediction using Twitter sentiment analysis. Standford University, CS229 (http:// cs229.stanford.edu/ proj2011/ GoelMittal-StockMarket PredictionUsingTwitterSentimentAnalysis.pdf ).
Nann, S., Krauss, J., & Schoder, D. (2013). Predictive analytics on public data - the case of stock markets. In Proceeding of 21st European Conference on Information Systems (paper 116).
Nofsinger, J. R. (2005). Social mood and financial economics. The Journal of Behavioral Finance, 6 (3), 144–160.
Oh, C. & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In Proceedings of the International Conference on Information Systems.
Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2), 1–135.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 (pp. 79–86). Association for Computational Linguistics.
Rao, T. & Srivastava, S. (2014). Twitter sentiment analysis: How to hedge your bets in the stock markets. In State of the Art Applications of Social Network Analysis (pp. 227–247). Springer.
Ruiz, E. J., Hristidis, V., Castillo, C., Gionis, A., & Jaimes, A. (2012). Correlating financial time series with micro-blogging activity. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (pp. 513–522). ACM.
Saif, H., He, Y., & Alani, H. (2012). Semantic sentiment analysis of Twitter. In The Semantic Web–ISWC 2012 (Vol. 7649, pp. 508–524). Lecture Notes in Computer Science. Springer.
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34 (1), 1–47.
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5 (4), 13–22.
Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013). Predictive sentiment analysis of tweets: A stock market application. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 77–88). Lecture Notes in
Computer Science Volume 7947. Springer Berlin Heidelberg.
Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2014). Stream-based active learning for sentiment analysis in the financial domain. Information Sciences, 285, 181–203.
Smailović, J., Grčar, M., & Žnidaršič, M. (2012). Sentiment analysis on tweets in a financial domain. In Proceedings of 4th Jožef Stefan International Postgraduate School Students Conference (pp. 169–175).
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2013). Tweets and trades: The information content of stock microblogs. European Financial Management.
Sul, H., Dennis, A. R., & Yuan, L. I. (2014). Trading on Twitter: The financial information content of emotion in social media. In 47th Hawaii International Conference on System Sciences (HICSS) (pp. 806–815). IEEE.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37 (2), 267–307.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62 (2), 406–418.
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61 (12), 2544–2558.
Trafalis, T. B., & Ince, H. (2000). Support vector machine for regression and applications to financial forecasting. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 348-353). IEEE.
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). Association for Computational Linguistics.
West, D., Dellana, S., & Qian, J. (2005). Neural network ensemble strategies for financial decision applications. Computers & operations research, 32(10), 2543-2559.
Xu, Z., Yu, K., Tresp, V., Xu, X., & Wang, J. (2003). Representative sampling for text classification using support vector machines. Springer.
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55 (4), 919–926.
Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55–62.
Zheludev, I., Smith, R., & Aste, T. (2014). When can social media lead financial markets? Scientific Reports, 4.
Spodnja zbirka vključuje sledeče članke (dobite jih s klikom na naslov članka)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Languages in Social Media (pp. 30–38). Association for Computational Linguistics.
Antweiler, W. & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59 (3), 1259–1294.
Argamon-Engelson, S. & Dagan, I. (1999). Committee-based sample selection for probabilistic classifiers. Journal of Artificial Intelligence Research, 11, 335–360.
Asur, S. & Huberman, B. A. (2010). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) (Vol. 1, pp. 492–499).
Bifet, A. & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. In Proceedings of the 13th International Conference on Discovery Science (DS) (pp. 1–15). Springer.
Bollen, J., Mao, H., & Pepe, A. (2011). Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media (ICWSM).
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2 (1), 1–8.
Bose, I., & Mahapatra, R. K. (2001). Business data mining—a machine learning perspective. Information & management, 39(3), 211-225.
Chen, R. & Lazer, M. (2011). Sentiment analysis of Twitter feeds for the prediction of stock market movement. CS 229 Machine Learning : Final Project.
Choudhry, R., & Garg, K. (2008). A hybrid machine learning system for stock market forecasting. World Academy of Science, Engineering and Technology, 39(3), 315-318.
Chung, J. E. & Mustafaraj, E. (2011). Can collective sentiment expressed on Twitter predict political elections? In Proceedings of the 25th AAAI Conference on Artificial Intelligence.
Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20 (3), 273–297.
Cover, T. & Hart, P. E. (1967). Nearest neighbor pattern classification. Information Theory, IEEE Transactions on, 13 (1), 21–27.
Fan, A., & Palaniswami, M. (2000). Selecting bankruptcy predictors using a support vector machine approach. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 354-359). IEEE.
Flach, P. (2012). Machine learning: The art and science of algorithms that make sense of data. Cambridge University Press.
Galindo, J., & Tamayo, P. (2000). Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications. Computational Economics, 15(1), 107-143.
Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1–12.
González-Ibáñez, R., Muresan, S., & Wacholder, N. (2011). Identifying sarcasm in Twitter: A closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2
(pp. 581–586). Association for Computational Linguistics.
Granger, C. W. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica: Journal of the Econometric Society, 424–438.
Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (pp. 78–87). ACM.
Haddi, E., Liu, X., & Shi, Y. (2013). The role of text pre-processing in sentiment analysis. Procedia Computer Science, 17, 26–32.
Jahanbakhsh, K. & Moon, Y. (2014). The predictive power of social media: On the predictability of US presidential elections using Twitter. arXiv preprint arXiv:1407.0622.
Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning (pp. 137–142).
Joachims, T. (1999). Making large-scale SVM learning practical. In B. Schölkopf, C. Burges, & A. Smola (Eds.), Advances in kernel methods - support vector learning (Chap. 11, pp. 169–184). Cambridge, MA: MIT Press.
Joachims, T. (2005). A support vector method for multivariate performance measures. In Proceedings of the 22nd International Conference on Machine Learning (pp. 377–384). ACM.
Joachims, T. (2006). Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 217–226). ACM.
Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767-2787.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI) - Volume 2 (pp. 1137–1143).
Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the OMG! In Proceedings of The International Conference on Weblogs and Social Media (ICWSM) (Vol. 11, pp. 538–541).
Kranjc, J., Podpečan, V., & Lavrač, N. (2012). Clowdflows: A cloud based scientific work-flow platform. In Machine Learning and Knowledge Discovery in Databases (pp. 816–819). Springer.
Kranjc, J., Podpečan, V., & Lavrač, N. (2013). Real-time data analysis in ClowdFlows. In Proceedings of The 2013 IEEE International Conference on Big Data (pp. 15–22). IEEE.
Kranjc, J., Smailović, J., Podpečan, V., Grčar, M., Žnidaršič, M., & Lavrač, N. (2014). Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Information Processing & Management.
doi:http://dx.doi.org/10.1016/j.ipm.2014.04.001
Lin, W. Y., Hu, Y. H., & Tsai, C. F. (2012). Machine learning in financial crisis prediction: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42(4), 421-436.
Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2, 627–666.
Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5 (1), 1–167.
Martineau, J. & Finin, T. (2009). Delta TFIDF: An improved feature space for sentiment analysis. In Proceedings of the Third AAAI Internatonal Conference on Weblogs and Social Media.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal.
Mishne, G. & Glance, N. S. (2006). Predicting movie sales from blogger sentiment. In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs (pp. 155–158).
Mittal, A. & Goel, A. (2012). Stock prediction using Twitter sentiment analysis. Standford University, CS229 (http:// cs229.stanford.edu/ proj2011/ GoelMittal-StockMarket PredictionUsingTwitterSentimentAnalysis.pdf ).
Nann, S., Krauss, J., & Schoder, D. (2013). Predictive analytics on public data - the case of stock markets. In Proceeding of 21st European Conference on Information Systems (paper 116).
Nofsinger, J. R. (2005). Social mood and financial economics. The Journal of Behavioral Finance, 6 (3), 144–160.
Oh, C. & Sheng, O. (2011). Investigating predictive power of stock micro blog sentiment in forecasting future stock price directional movement. In Proceedings of the International Conference on Information Systems.
Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1-2), 1–135.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing - Volume 10 (pp. 79–86). Association for Computational Linguistics.
Rao, T. & Srivastava, S. (2014). Twitter sentiment analysis: How to hedge your bets in the stock markets. In State of the Art Applications of Social Network Analysis (pp. 227–247). Springer.
Ruiz, E. J., Hristidis, V., Castillo, C., Gionis, A., & Jaimes, A. (2012). Correlating financial time series with micro-blogging activity. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining (pp. 513–522). ACM.
Saif, H., He, Y., & Alani, H. (2012). Semantic sentiment analysis of Twitter. In The Semantic Web–ISWC 2012 (Vol. 7649, pp. 508–524). Lecture Notes in Computer Science. Springer.
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM computing surveys (CSUR), 34 (1), 1–47.
Shearer, C. (2000). The CRISP-DM model: The new blueprint for data mining. Journal of Data Warehousing, 5 (4), 13–22.
Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2013). Predictive sentiment analysis of tweets: A stock market application. In Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data (pp. 77–88). Lecture Notes in
Computer Science Volume 7947. Springer Berlin Heidelberg.
Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2014). Stream-based active learning for sentiment analysis in the financial domain. Information Sciences, 285, 181–203.
Smailović, J., Grčar, M., & Žnidaršič, M. (2012). Sentiment analysis on tweets in a financial domain. In Proceedings of 4th Jožef Stefan International Postgraduate School Students Conference (pp. 169–175).
Sprenger, T. O., Tumasjan, A., Sandner, P. G., & Welpe, I. M. (2013). Tweets and trades: The information content of stock microblogs. European Financial Management.
Sul, H., Dennis, A. R., & Yuan, L. I. (2014). Trading on Twitter: The financial information content of emotion in social media. In 47th Hawaii International Conference on System Sciences (HICSS) (pp. 806–815). IEEE.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37 (2), 267–307.
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62 (2), 406–418.
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61 (12), 2544–2558.
Trafalis, T. B., & Ince, H. (2000). Support vector machine for regression and applications to financial forecasting. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Vol. 6, pp. 348-353). IEEE.
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on association for computational linguistics (pp. 417–424). Association for Computational Linguistics.
West, D., Dellana, S., & Qian, J. (2005). Neural network ensemble strategies for financial decision applications. Computers & operations research, 32(10), 2543-2559.
Xu, Z., Yu, K., Tresp, V., Xu, X., & Wang, J. (2003). Representative sampling for text classification using support vector machines. Springer.
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55 (4), 919–926.
Zhang, X., Fuehres, H., & Gloor, P. A. (2011). Predicting stock market indicators through Twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55–62.
Zheludev, I., Smith, R., & Aste, T. (2014). When can social media lead financial markets? Scientific Reports, 4.
machine_learning_bibliography.pdf | |
File Size: | 195 kb |
File Type: |
Spodaj je še nekaj najbolj znanih monografij s področja strojnega učenja, ponovno s povezavami do polnih besedil:
Feldman, R., Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press.
Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Berlin: Springer Verlag.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York Heidelberg Dordrecht London: Springer.
Vapnik, V. (1998). Statistical learning theory. New York: John Wiley & Sons.
Feldman, R., Sanger, J. (2007). The text mining handbook: Advanced approaches in analyzing unstructured data. Cambridge: Cambridge University Press.
Hastie, T., Tibshirani, R., Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference and Prediction. Berlin: Springer Verlag.
James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. New York Heidelberg Dordrecht London: Springer.
Vapnik, V. (1998). Statistical learning theory. New York: John Wiley & Sons.