===== Ph. D. Dissertation ===== The long title translates as //A theoretical approach to connectionist learning, with applications for speech recognition//. This is a relatively big document with a number of relatively new ideas for the time. * [[wp>Connectionism|Connectionist learning algorithms]] can be studied as [[wp>stochastic approximations]] and more specifically [[wp>stochastic gradient descent]] algorithms, possibly involving surrogate loss functions (chapters 2 and 3). * The performance of these algorithms can be studied using advanced statistical theories such as [[wp>Vladimir Vapnik]]'s [[wp>Structural Risk Minimization]] (chapter 4). * The speed of these algorithms can be approached using the methods of numerical optimization (chapter 5.) * Complex applications, such as [[wp>speech recognition]], can be addressed using modular learning systems such as Discriminant Hidden Markov Models (chapter 8 and 9). * Probabilistic discriminant modular learning systems are often limited by the so-called //label-bias problem// (chapter 10). Finding a solution took a [[:research:structured|three more years]]. ==== ==== Léon Bottou: //**Une Approche théorique de l'Apprentissage Connexionniste: Applications à la Reconnaissance de la Parole**//, Ph.D. thesis, Université de Paris XI, Orsay, France, 1991. [[http://leon.bottou.org/publications/djvu/bottou-1991.djvu|bottou-1991.djvu (one big file)]] [[http://leon.bottou.org/publications/pdf/bottou-1991.pdf|bottou-1991.pdf]] [[http://leon.bottou.org/publications/psgz/bottou-1991.ps.gz|bottou-1991.ps.gz]]\\ [[http://leon.bottou.org/cgi/djvuserve/publications/djvu/bottou-1991.djvu|bottou-1991.djvu (served page per page)]] @phdthesis{bottou-91a, title = {Une Approche th\'eorique de l'Apprentissage Connexionniste: Applications \`a la Reconnaissance de la Parole}, author = {Bottou, {L\'eon}}, year = {1991}, school = {Universit\'{e} de Paris XI}, address = {Orsay, France}, url = {http://leon.bottou.org/papers/bottou-91a}, }