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DATaSets

Bi-density twin support vector machines

In this paper we present a classifier called bi-density twin support vector machines (BDTWSVMs) for data classification. In the training stage, BDTWSVMs first compute the relative density degrees for all training points using the intra-class graph whose weights are determined by a local scaling heuristic strategy, then optimize a pair of nonparallel hyperplanes through two smaller sized support vector machine (SVM)-typed problems. In the prediction stage, BDTWSVMs assign to the class label depending on the kernel density degree-based distances from each test point to the two hyperplanes. BDTWSVMs not only inherit good properties from twin support vector machines (TWSVMs) but also give good description for data points. The experimental results on toy as well as publicly available DATaSets indicate that BDTWSVMs compare favorably with classical SVMs and TWSVMs in terms of generalization

標(biāo)簽： recognition Bi-density machines support pattern vector twin for

上傳時間： 2019-06-09

上傳用戶：lyaiqing
Machine learning

Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many DATaSets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.

標(biāo)簽： learning Machine

上傳時間： 2020-06-10

上傳用戶：shancjb
《Python深度學(xué)習(xí)》2018中文版+源代碼

這是我在做大學(xué)教授期間推薦給我學(xué)生的一本書，非常好，適合入門學(xué)習(xí)。《python深度學(xué)習(xí)》由Keras之父、現(xiàn)任Google人工智能研究員的弗朗索瓦?肖萊（Franc?ois Chollet）執(zhí)筆，詳盡介紹了用Python和Keras進行深度學(xué)習(xí)的探索實踐，包括計算機視覺、自然語言處理、產(chǎn)生式模型等應(yīng)用。書中包含30多個代碼示例，步驟講解詳細透徹。作者在github公布了代碼，代碼幾乎囊括了本書所有知識點。在學(xué)習(xí)完本書后，讀者將具備搭建自己的深度學(xué)習(xí)環(huán)境、建立圖像識別模型、生成圖像和文字等能力。但是有一個小小的遺憾：代碼的解釋和注釋是全英文的，即使英文水平較好的朋友看起來也很吃力。本人認為，這本書和代碼是初學(xué)者入門深度學(xué)習(xí)及Keras最好的工具。作者在github公布了代碼，本人參照書本，對全部代碼做了中文解釋和注釋，并下載了代碼所需要的一些數(shù)據(jù)集（尤其是“貓狗大戰(zhàn)”數(shù)據(jù)集），并對其中一些圖像進行了本地化，代碼全部測試通過。（請按照文件順序運行，代碼前后有部分關(guān)聯(lián)）。以下代碼包含了全書約80%左右的知識點，代碼目錄：2.1: A first look at a neural network（初識神經(jīng)網(wǎng)絡(luò)）3.5: Classifying movie reviews（電影評論分類：二分類問題）3.6: Classifying newswires（新聞分類：多分類問題）3.7: Predicting house prices（預(yù)測房價：回歸問題）4.4: Underfitting and overfitting（過擬合與欠擬合）5.1: Introduction to convnets（卷積神經(jīng)網(wǎng)絡(luò)簡介）5.2: Using convnets with small DATaSets（在小型數(shù)據(jù)集上從頭開始訓(xùn)練一個卷積網(wǎng)絡(luò)）5.3: Using a pre-trained convnet（使用預(yù)訓(xùn)練的卷積神經(jīng)網(wǎng)絡(luò)）5.4: Visualizing what convnets learn（卷積神經(jīng)網(wǎng)絡(luò)的可視化）

標(biāo)簽： python 深度學(xué)習(xí)

上傳時間： 2022-01-30

上傳用戶：

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